CN100547594C - A Digital Earth Prototype System - Google Patents

Abstract

The invention relates to the technical field of digital earth, and discloses a digital earth prototype system, which includes: a data receiving and fast processing subsystem, a metadata service subsystem, a model library subsystem, a spatial information database subsystem, and a grid calculation subsystem system, map application service subsystem and virtual reality subsystem. Utilizing the present invention, the data resource sharing platform, the knowledge sharing platform, the computing resource sharing platform and the collaborative work platform of the digital earth prototype system are constructed, and through the establishment of the prototype system, the key technologies related to the construction of the scientific service platform for spatial data applications are solved , forming a service system for geoscience spatial information applications.

Description

A Digital Earth Prototype System

technical field

The invention relates to the technical field of digital earth, in particular to a method of optimizing and integrating various historical data, model algorithms and technologies, establishing a comprehensive dynamic model of multi-scale elements in a global context, realizing a new leap in disciplines, and utilizing digital earth metadata management , The multi-dimensional space-time scale structure framework is a digital earth prototype system that performs multi-level, multi-variable correlation analysis and historical reconstruction of evolution laws for the sustainable development of the earth, and makes predictions and predictions for future development.

Background technique

Digital Earth is a concept proposed by former US Vice President Al Gore in the report "Digital Earth - Understanding Our Planet in the 21st Century" on January 31, 1998. The characteristics of the era of scientific research, study and life.

Digital Earth is a hotspot in the current academic research and belongs to the frontier issue of science. The digital earth is a virtual earth constructed by massive, multi-resolution, multi-temporal, multi-type space earth observation data and socio-economic data and its analysis algorithms and models.

At present, although there are many relevant researches at home and abroad, most of them are only in the exploration stage of relevant theories, and few of them have put theories into practice. The representative research mainly includes NASA's digital earth demonstration system and the Alexandria digital earth prototype.

NASA's digital earth demonstration system is mainly used in map service functions, and the application field is relatively narrow. As shown in Fig. 1, Fig. 1 shows a schematic diagram of the architecture of the NASA digital earth demonstration system.

The goal of the Alexander Digital Earth Prototype (ADEPT) is to build a Distributed Digital Library (DDL) that supports the creation and use of services for personalized collections of geospatial related information, which is quite different from the research purpose of Digital Earth. As shown in Figure 2, Figure 2 shows a schematic diagram of the architecture of the Alexandria Digital Earth prototype.

In addition, although some software manufacturers at home and abroad have developed some digital earth prototype systems, their goals and service value are mainly reflected in map search and other auxiliary business services. At the same time, there is no very standardized digital earth prototype plan in China at present, and no network service provider provides similar information services.

Contents of the invention

(1) Technical problems to be solved

In view of this, the main purpose of the present invention is to provide a digital earth prototype system to establish a scientific service platform for the digital earth prototype system, on this basis to carry out theoretical analysis of digital earth, establish a framework and model of digital earth, the key to research and development Technologies and methods, demonstrating application capabilities in different fields, providing theoretical and technical support for the digital earth strategy, and gradually forming data resource sharing platforms, knowledge sharing platforms, computing resource sharing platforms and collaborative work platforms to solve scientific services for spatial data applications The key technologies related to platform construction form a service system for geoscience spatial information applications.

(2) Technical solution

In order to achieve the above purpose, the present invention provides a digital earth prototype system, the digital earth prototype system includes:

The data receiving and rapid processing subsystem is used to receive and process the data emitted by the spaceborne medium-resolution imaging spectrometer MODIS, and quickly process and store it, providing the basic data source for the experiment and application of other subsystems in the digital earth prototype system;

The metadata service subsystem is used to manage the metadata information in the database of the entire system, and provide services for the system to use the metadata information in the database reasonably and quickly;

The model library subsystem is used to provide general models of algorithms and applications for the spatial data processing and analysis of the digital earth prototype system;

The spatial information database subsystem is used to manage the spatial data in the digital earth prototype system, standardize the processing, extraction and analysis of objectively simulated and reflected real-world spatial data, and form a database suitable for large-scale applications in multiple fields;

The grid computing subsystem is used to obtain data from remote data servers or locally according to user needs, trigger services and monitor their running status, divide a task into several subtasks, and then assign subtasks to computers in the grid computing pool Execute, recycle and integrate the execution results, and return the results to the user;

The map application service subsystem is used to realize the visualization of global information in the Internet environment;

The virtual reality subsystem is used to use artificial intelligence and pattern recognition technology to model, learn, plan and calculate according to the established domain knowledge base and database, and realize visual, tactile, auditory and dynamic virtual reality simulations.

In the above scheme, the data receiving and fast processing subsystem includes:

The ground station control system module is used to control and monitor the operating conditions of various parts of the ground station, and operates in the form of direct commands or pre-established procedures;

Satellite acquisition and tracking module, used to realize satellite orbit report calculation, orbit parameter display and orbit selection;

Data receiving and quick view module, used to realize data receiving and data quick view;

The data processing module is used to unpack and regularize the received and preprocessed raw data, perform geographic positioning and calibration correction processing, and generate MODIS format data of 0-level, 1A-level, and 1B-level standards;

The data production module is used to generate level 0, level 1A, and level 1B MODIS standard data products, of which the format of level 1B products is the standard Earth Observation System/layered data format EOS/HDF, and provides remote sensing applications for land, ocean and atmosphere advanced data products;

The data playback and editing module is used to playback, display and edit the generated data products;

The data distribution and backup module is used for intelligent archiving, distribution, management and backup of all levels of data received and processed by the system;

Application software modules for geometrically accurate correction of MODIS data, national mosaic map of MODIS data and overlay of administrative boundaries, fire monitoring, flood monitoring, drought monitoring, surface temperature inversion, vegetation index calculation, cloud detection, ocean water color and snow cover monitoring information.

In the above scheme, for the way of pre-establishing the program, the ground station control system module provides a user menu of the graphical interface, which is used to realize the setting of the receiving task of multiple satellites and multiple orbits, the display of the task schedule and execution status, and the receiving process. Real-time display of various state parameters, event information record file and browsing, subsystem state configuration and operation, and disk file management.

In the above scheme, the satellite capture and tracking module calculates the satellite orbit report by obtaining two lines of parameters when realizing the satellite orbit report calculation; when realizing the orbit parameter display, displays all the orbit parameters of the transit satellite in text mode; When implementing orbit selection, the user can arbitrarily select the desired satellite orbit and its corresponding parameters according to the needs.

In the above scheme, when the data receiving and quick viewing module realizes data receiving, according to the selected transit satellite orbit parameters, it automatically receives satellite signals according to the satellite orbit schedule, and performs at least amplification, frequency conversion, and resolution on the received signals. Preprocessing of adjustment, output and storage to obtain raw data RAW DATA;

The data receiving and quick view module displays the transit satellite orbit, the currently receiving orbit and its progress when realizing the data quick view, and displays the received data in the form of a color image in real time.

In the above solution, the data playback and editing module editing the generated data products includes:

Image synthesis, used for multi-channel synthesis display of arbitrary band data to produce color images;

Image enhancement, used to enhance the display image;

Data projection transformation, which is used to perform projection transformation on the image including at least Lambert, polar eclipse, Mercator, equilongation, and Gauss-Krüger, and display the transformed image in real time;

Layer overlay, used to overlay geographic information layers including at least latitude and longitude grids, administrative divisions, and geographical indications on the image;

Geographical information addition, used to add common graphic symbols and annotation characters on the image, and edit the added graphic symbols and annotation characters;

Image data extraction, used to segment images, mine images according to province, city boundaries or custom boundaries, and extract data;

Data format conversion, used to output HDF data in image format file according to bitmap graphic file BMP or binary format;

Image stitching, used for multi-track, multi-day data stitching synthesis and display based on regional datasets.

In the above solution, the application software module is based on the algorithms and standards announced by NASA, has image processing functions, and is oriented to remote sensing applications. It adopts an open high-level data product design framework, and discloses algorithms and standards.

In the above solution, the metadata service subsystem includes:

The spatial metadata gateway is used to realize the information interaction between the client and the server;

The spatial metadata server is used to receive the information from the spatial metadata gateway, call the corresponding function module after parsing, and if the result set needs to be returned, organize the result and return it to the client in the form of an Extensible Markup Language XML document end, and is responsible for the release of spatial metadata on the Internet;

Spatial metadata database manager, used for the collection, storage, management and maintenance of spatial metadata;

Spatial metadata query tool, which is used to provide an interface for clients to manage metadata schema information, metadata records and various mapping relationships, including adding, deleting, modifying and browsing metadata records;

The spatial metadata management tool is composed of a user interface module and a protocol processing and transmission module. The user interface module is used to interact with users, input query conditions and present query results; the protocol processing and transmission module is used to collect query parameters collected by the user interface module Organize query statements, send them to the spatial metadata server through TCP/IP protocol, then query the spatial metadata database, and submit the query results to the user interface module for display.

Among the above schemes, the improved homomorphic filtering cloud removal model, the pyramid structure remote sensing data fast compression and playback model, the multi-scale remote sensing image wavelet fusion model, the hyperspectral remote sensing image cube model, the surface temperature inversion model, and the spline multi-scale image representation And registration model, radar data soil moisture inversion model, vegetation index model biomass model, net primary productivity model, microwave remote sensing data ground parameter inversion model and radar interferometry INSAR model.

In the above scheme, the spatial information database subsystem includes:

Spatial data processing module, used to realize control bottom data processing, bottom control framework layout and bottom data accuracy inspection;

The remote sensing data processing module is used to realize the establishment of the control point library with the same name, the measurable precision processing inspection and the establishment of the image metadata database;

The feature information processing module is used to restore and enhance colors, image mosaic color matching, non-homologous data assimilation and multi-scale data fusion;

Thematic information analysis module is used to realize various remote sensing application analysis based on standard remote sensing data platform for remote sensing data results.

In the above solution, the grid computing subsystem includes:

The process control module is used to control the process by using workflow technology as a unit;

Grid middleware, which is used to centrally manage and schedule underlying hardware computing resources, distribute tasks submitted by the upper layer to available computers for calculation according to a certain strategy, and recycle the results;

The initialization module is used to prepare for special processing, including data acquisition, log file creation, remote data acquisition, decompression, subtask segmentation and packaging, data format conversion and interface conversion;

Thematic processing module, used to provide nodes with surface temperature inversion, aerosol optical depth inversion, normalized difference vegetation index, enhanced vegetation index, soil adjusted vegetation index, land cover information classification, cloud detection, geometric correction, image clipping cutting, image stitching and image matching;

The post-processing module is used to combine the calculation results of subtasks to generate HDF files, compress them, and put them into the result database.

In the above scheme, in the thematic processing module,

The inversion of the surface temperature includes: realizing the inversion of the surface temperature by using the thermal infrared data of MODIS through the adaptive split window method of moving the window;

The inversion of the aerosol optical depth includes: using the dark pixel method to extract the aerosol optical depth parameter of the MODIS image, so as to realize the inversion of the aerosol optical depth;

Described normalized vegetation index comprises: first carry out geometric correction to MODIS data, then generate NDVI by single data, realize normalized vegetation index;

The enhanced vegetation index includes: first geometrically correcting the MODIS data, and then generating EVI through a single piece of data to realize the enhanced vegetation index;

The soil-adjusted vegetation index includes: first geometrically correcting the MODIS data, and then generating SAVI through a single piece of data to realize the soil-adjusted vegetation index;

The classification of the land cover information includes: first geometrically correcting the MODIS data, and then using the multi-band information of the remote sensing image to perform non-supervised classification to realize the classification of the land cover information;

Described cloud detection comprises: first carry out geometric correction to MODIS data, then filter out the cloud in the image, realize cloud detection;

The geometric correction includes: using the 1KM resolution longitude and latitude information in the 1B data of MODIS 500 meter resolution as control points, the MODIS data of 1KM resolution is geometrically corrected, and the gray scale resampling method adopts the method of nearest interpolation to realize Geometric Correction;

The image clipping includes: first geometrically correcting the MODIS data, and then cutting out a smaller research area from the large image to realize image clipping;

Described image mosaic comprises: first carry out geometric correction to MODIS data, then through the geographical coordinate data inside MODIS data, a plurality of adjacent pieces of MODIS 1B data images are mosaiced into a bigger complete image, realizes image mosaic;

The image matching includes: first geometrically correcting the MODIS data, and then obtaining the same area data through the positioning information of each pixel obtained as a result of the geometric correction, so as to generate multiple matched images of the same area, and realize Image matching.

In the above solution, the map application service subsystem manages spatial data and attribute data through node-based attribute binding, hierarchical management technology and R-tree technology, and adopts skin + skeleton technology, with the help of distributed storage, distributed Computing technology realizes the storage, dynamic loading and display of massive data, including at least the visualization module and query module, which are the information windows of the prototype system platform for external services.

In the above solution, the visualization module is used to control the map through menu commands to perform stepless zoom-in. With the step-by-step zoom-in, images with larger resolutions in the display area are called and observed from different angles according to the specified route. Browsing, from a global overview to a detailed observation of a town or even a smaller area, roaming anywhere in the world through the keyboard, mouse or navigation ball;

The query module is used to realize data query of basic spatial data, layered and comprehensive display of various levels of vector graphics and basic topographic maps, and bidirectional spatial query of graphic information and attribute information in the GIS system.

In the above scheme, the virtual reality subsystem includes:

The mass data management function module is used to realize the management of the real-time collected data, and retain and organize the historical data within a period of time;

The real-time roaming browsing and editing function module of large geographical landscapes is used to realize the real-time roaming browsing and editing of large geographical landscapes;

The 3D analysis and display function module is used to provide the interface of GIS analysis function, comprehensive 3D analysis function, query terrain data, perform statistical analysis on terrain data, support various ways of terrain measurement in a visual way, and measure Results are visualized in an intuitive way;

The visualization function module of the digital simulation model is used to realize the visualization of the digital simulation model;

The visualization function module of engineering construction models and schemes is used to realize the visualization of engineering construction models and schemes.

In the above solution, the real-time roaming browsing and editing function module of the large-scale geographical landscape supports multi-view viewing and full-screen browsing, history playback, screen capture, flight route editing, and target information query to achieve a real visual simulation effect.

(3) Beneficial effects

As can be seen from the foregoing technical solutions, the present invention has the following beneficial effects:

1. This digital earth prototype system provided by the present invention is a (data) acquisition of multi-resolution, multi-temporal, multi-type space earth observation data, geoscience data and related social and economic data, and establishes a system based on geographical coordinates. Based on a distributed database system with massive storage, and using scientific computing, network, and virtual reality technologies, it is a model system that realizes the digital reproduction and prediction of the real earth and its related phenomena. Through the establishment and application demonstration of the prototype system, the digital earth prototype system builds the data resource sharing platform, knowledge sharing platform, computing resource sharing platform and collaborative work platform of the digital earth prototype system, and through the establishment of the prototype system, solves the problem of spatial data The key technologies related to the construction of the applied scientific service platform have formed a service system for the application of geoscience spatial information.

2. The digital earth prototype system provided by the present invention has created a large-scale advanced software and hardware environment as the core, and consists of seven subsystems including data reception, metadata, model library, grid computing, spatial information library, map service and virtual reality It is a large-scale spatial information service platform based on the network and referred to by national and industry standards. It constructs the conceptual model of digital earth and the theoretical framework of digital earth, and puts forward the interactive thinking of real earth, conscious earth and digital earth. The system independently developed digital earth data mining algorithms and inversion software, developed geoscience inversion software, developed a variety of data fusion technologies, and built a stepless scale geographic information system (GIS) information synthesis model.

3. The digital earth prototype system provided by the present invention combines grid technology and workflow technology for data processing of the prototype system, so that computing resources and data processing algorithms and models are widely shared, saving time and effort. Economic resources; adopting terabyte (TB) server cluster technology, which solves the bottleneck problem of network GIS transmission and ensures the efficient operation of large data volume map service applications; integrates international advanced image compression technology, forming a massive image data compression and management Ability; established a control point library with the same name for precise calibration, which improved the overall accuracy of digital products and provided conditions for the organic connection between domestic digital products and international digital products.

4. This digital earth prototype system provided by the present invention builds a measurable standard digital platform, improves the standardization level of digital products; realizes the interaction between different virtual environments, virtual reality and geographic information software and based on paging Real-time display of large terrain data with Level of Detail/Continuous Level of Detail (LOD/CLOD) technology, developed a network 3D browsing system; the data receiving system has a wide capture and tracking bandwidth, the information code rate is continuously variable, and the system has a high degree of automation. It solves the problem of missing targets at high elevation angles of polar-orbiting satellites and realizes unattended operation. The receiving subsystem and the prototype system are seamlessly embedded and can be operated in different places.

5. The digital earth prototype system provided by the present invention is a highly integrated system. A set of comprehensive system integration specifications based on national and industry standards has been formulated to form a digital earth working platform and realize the sharing of spatial information .

6. In the digital earth prototype system provided by the present invention, the data receiving and fast processing subsystem realizes automatic receiving processing and data archiving, real-time visualization of received data, and stable image quality. The spatial information database subsystem has established a fine-calibration control point library with the same name, provided various algorithms for massive computing of large-scale remote sensing data, realized the organic connection of digital products in the international coordinate system, and built a measurable standard digital platform. The model library integrates multiple models such as image preprocessing, feature inversion, data fusion and data processing, and provides effective technical methods for further mining and extraction of remote sensing information. The map application service subsystem realizes layer control, zooming, multi-layer data representation, registration and display of image graphics, realizes effective query of spatial data and attribute data, and has functions such as length and area measurement and buffer analysis. The virtual reality subsystem has established a geographical landscape database and a thematic database dedicated to simulation, realizing real-time roaming browsing and editing, inundation analysis in a 3D environment, calculation of excavation and filling, and other functions, and realizing the functions of 3D scene network publishing, browsing, and information query. .

Description of drawings

Figure 1 is a schematic diagram of the architecture of the NASA Digital Earth Demonstration System;

Figure 2 is a schematic diagram of the architecture of the Alexandria Digital Earth prototype;

Fig. 3 is the structural block diagram of the digital earth prototype system provided by the present invention;

Fig. 4 is a system block diagram of the data receiving and fast processing subsystem in the digital earth prototype system provided by the present invention;

Fig. 5 is a block diagram of the metadata service subsystem in the digital earth prototype system provided by the present invention;

Fig. 6 is a schematic diagram of the operation process of the metadata service subsystem in the digital earth prototype system provided by the present invention;

7 is a schematic diagram of the logic structure of the grid computing subsystem and external modules in the digital earth prototype system provided by the present invention;

Fig. 8 is a schematic diagram of the logical structure of the grid computing subsystem and internal modules in the digital earth prototype system provided by the present invention;

Fig. 9 is a schematic diagram of the data flow structure of the grid computing subsystem in the digital earth prototype system provided by the present invention;

Fig. 10 is a schematic diagram of the control flow structure of the grid computing subsystem in the digital earth prototype system provided by the present invention;

Fig. 11 is a schematic diagram of the service flow of the map application service subsystem in the digital earth prototype system provided by the present invention;

Fig. 12 is a frame structure diagram of the digital earth prototype system provided by the present invention;

Fig. 13 is a schematic diagram of three basic functional layers of the digital earth prototype system provided by the present invention;

Fig. 14 is a schematic diagram of the frame structure of the digital earth prototype system provided by the present invention;

Fig. 15 is a schematic diagram of the architecture of the digital earth prototype system provided by the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

This digital earth prototype system provided by the present invention is a (data) acquisition of multi-resolution, multi-temporal and multi-type space observation data, geoscience data and related social and economic data, and establishes a system based on geographic coordinates. , a distributed database system with massive storage, and a model system that uses scientific computing, network, and virtual reality technologies to realize the digital reproduction and prediction of the real earth and its related phenomena. Through the establishment and application demonstration of the prototype system, the digital earth prototype system builds the data resource sharing platform, knowledge sharing platform, computing resource sharing platform and collaborative work platform of the digital earth prototype system, and through the establishment of the prototype system, solves the problem of spatial data The key technologies related to the construction of the applied scientific service platform have formed a service system for the application of geoscience spatial information.

As shown in Figure 3, Figure 3 is a structural block diagram of the digital earth prototype system provided by the present invention, the digital earth prototype system includes a data receiving and fast processing subsystem 10, a metadata service subsystem 11, a model library subsystem 12, a space Information database subsystem 13 , grid computing subsystem 14 , map application service subsystem 15 and virtual reality subsystem 16 .

Among them, the data receiving and fast processing subsystem 10 is used to receive and process the data emitted by the spaceborne medium-resolution imaging spectrometer (MODIS), and quickly process and put it into the warehouse, and provide the basic data for the experiment and application of other subsystems in the digital earth prototype system source.

The metadata service subsystem 11 is used to manage the metadata information in the database of the whole system, and provide services for the system to use the metadata information in the database reasonably and quickly.

The model library subsystem 12 is used to provide general models of algorithms and applications for the spatial data processing and analysis of the digital earth prototype system.

The spatial information database subsystem 13 is used to manage the spatial data in the digital earth prototype system, standardize processing, extraction and analysis of objectively simulated and reflected real-world spatial data, and form a database suitable for multi-field large-scale applications.

The grid computing subsystem 14 is used to obtain data from remote data servers or locally according to user needs, trigger services and monitor their running status, divide a task into several subtasks, and then distribute subtasks to computers in the grid computing pool Execute, recycle and integrate the execution results, and return the results to the user.

The map application service subsystem 15 is used to realize the visualization of global information in the Internet environment.

The virtual reality subsystem 16 is used to use artificial intelligence and pattern recognition technology to perform modeling, learning, planning and calculation according to the established domain knowledge base and database, so as to realize visual, tactile, auditory and dynamic virtual reality simulation.

After four years of construction, the digital earth prototype system version 1.0 has completed the establishment of multiple subsystems such as data reception and fast processing, grid computing, spatial information database, metadata service, model library, map service and virtual reality. In the process, from data acquisition to data analysis and display expression, the subsystems are closely connected to form a digital earth work platform. The digital earth prototype system provided by the present invention will be further described in detail below in conjunction with the accompanying drawings.

As for the data receiving and fast processing subsystem, the subsystem aims at receiving and processing MODIS data in the spaceborne, uses advanced integration technology, integrates domestic and foreign products, builds the aerospace data support system of the digital earth prototype system, and develops The vast market of MODIS integration technology and data products. The subsystem is divided according to its functions. There are eight functional modules from antenna reception to data generation. They are the ground station control system module, satellite acquisition and tracking module, data reception and quick view module, data processing module, and data production module. , data playback and editing module, data distribution and backup module and application software module.

Among them, the ground station control system module is used to control and monitor the operating conditions of various parts of the ground station, and operates in the form of direct commands or pre-established programs. For the method of pre-establishing the program, the ground station control system module provides a user menu of a graphical interface, which is used to realize multi-satellite multi-orbit receiving task setting, task schedule and execution status display, and various status parameters in the receiving process Real-time display, event information record file and browsing, subsystem status configuration operation and disk file management. In addition, the ground station control system module controls and monitors the operation status of various parts of the ground station. It can be run as a direct command or as a pre-programmed program. For the method of pre-establishing the program, a user menu of the graphical interface can be provided, so that this method can be used to monitor and control all aspects of the entire receiving system.

The satellite acquisition and tracking module is used to realize satellite orbit report calculation, orbit parameter display and orbit selection. When the satellite acquisition and tracking module realizes the calculation of the satellite orbit report, it calculates the satellite orbit report by obtaining two lines of parameters; when realizing the display of the orbit parameters, it displays all the orbit parameters of the transiting satellites in text; when realizing the orbit selection, Users can arbitrarily choose the satellite orbits they want to receive and their corresponding parameters according to their needs.

The data receiving and quick view module is used to realize data receiving and data quick view. When realizing data reception, the data receiving and quick view module automatically receives satellite signals according to the selected transit satellite orbit parameters and satellite orbit timetable, and performs at least amplification, frequency conversion, demodulation, output and storage on the received signals. Preprocessing to obtain raw data (RAW DATA). The data receiving and quick view module displays the transit satellite orbit, the currently receiving orbit and its progress when realizing the data quick view, and displays the received data in the form of color images in real time.

The data processing module is used to unpack and regularize the received and preprocessed raw data, geolocate and calibrate and correct, and generate MODIS format data of 0-level, 1A-level, and 1B-level standards.

The data production module is used to generate level 0, level 1A, and level 1B MODIS standard data products, of which level 1B product format is the standard Earth Observation System/Hierarchical Data Format (EOS/HDF), and provides remote sensing for land, ocean and atmosphere Applied advanced data products.

The data playback and editing module is used to playback, display and edit the generated data products. The data playback and editing module edits the generated data products, including image synthesis, image enhancement, data projection transformation, layer overlay, geographic information addition, image data extraction, data format conversion and image splicing. Image synthesis is used to perform multi-channel composite display of arbitrary band data to produce color images; image enhancement is used to enhance the displayed image; data projection transformation is used to perform at least Lamberto, polar eclipse, and Mercator on the image , equal longitude and latitude, and Gauss-Krüger projection transformation, and display the transformed image in real time; layer overlay is used to overlay geographic information layers including at least latitude and longitude grids, administrative divisions, and geographical indications on the image; geographic information addition is used to Add commonly used graphic symbols and annotation characters on the image, and edit the added graphic symbols and annotation characters; image data extraction is used to segment the image, dig the image according to the province, city boundary or custom boundary, and extract data ; Data format conversion is used to output HDF data in image format according to bitmap graphics file (BMP) or binary format; image mosaic is used for multi-track, multi-day data mosaic synthesis and display based on regional datasets.

The data distribution and backup module is used for intelligent archiving, distribution, management and backup of all levels of data received and processed by the system.

The application software module is used to provide precise geometric correction of MODIS data, national mosaic map of MODIS data and overlay of administrative boundaries, fire monitoring, flood monitoring, drought monitoring, surface temperature inversion, vegetation index calculation, cloud detection, ocean water color and snow cover monitoring information . The application software module is based on the algorithms and standards announced by NASA, has image processing functions, and is oriented to remote sensing applications. It adopts an open design framework for advanced data products, open algorithms, and open standards.

The data receiving and fast processing subsystem can also be composed of the following subsystems in terms of structure: antenna and control subsystem, channel subsystem, GPS time code unit, data intake subsystem, receiving and processing platform subsystem, Data recording and archiving subsystems and high-end data product development and application software subsystems.

1. Antenna and control subsystem

It is mainly composed of antenna, feed, servo and control unit. The antenna structure adopts X/Y type mount and no counterweight technology. The control system consists of Antenna Control Unit (ACU), Antenna Drive Unit (ADU), and Axis Angle Encoding Unit. The step tracking receiver detects the intermediate frequency signal sent by the down-conversion, and the ACU performs track step automatic tracking according to the signal level to improve the accuracy of the antenna tracking target. The X/Y mount ensures the overhead tracking capability of the system.

Subsystem technical indicators:

Caliber: 3.2M

Working frequency: 8.0GHz～8.5GHz

Antenna gain: >46dB

First side lobe: better than -15dB

Polarization mode: circular polarization, RHC, LHC can be switched

Antenna movement range: X: 0 degrees to 180 degrees

Y: 0 degrees to 180 degrees

Movement speed: X: 0°～6°/sec

Y: 0°～6°/sec

Motion acceleration: X: 0°~6°/sec*sec

Y: 0°～6°/sec*sec

Servo resolution: better than 0.01 degrees

Positioning accuracy: better than 0.07(rms)

Way of working:

- standby

- Manual

- command prefab

——Waiting for prefabrication

- track program tracking

——remote control

Control protection requirements: X-Y pre-limit cut-off power protection

X-Y motor brake braking

Spring time cushioning absorbs final overshoot energy

Environmental requirements: working humidity: -40 degrees to 60 degrees

Humidity: 10~100%

Wind speed resistance: less than or equal to 28.4m/s to ensure precision work

Less than or equal to 36.7m/s can work

Less than or equal to 54m/s preservation collection

2. Channel subsystem

It is mainly composed of low-noise amplifier, up-converter, down-converter and demodulator.

The low-noise amplifier LNA and the first down-converter are installed on the antenna to ensure that the loss of the 1GHZ signal transmitted from the antenna to the receiving room is relatively small.

DM-D-3 signal demodulator adopts advanced digital demodulation technology, which has the characteristics of low power consumption, small size, anti-interference and high reliability.

Technical indicators:

A. Noise Amplifier (LAN)

Frequency range: 8000Mhz～8500Mhz, bandwidth 500Mhz

Noise figure: less than or equal to 1.3dB

Input interface: BJ84 glass guide flange

Output interface: SMA

B. First stage down converter

Input frequency: 8000Mhz～8500Mhz

Output frequency: 750Mhz～1250Mhz

Gain: greater than or equal to 10dB

Input interface: SMA

Output interface: L16

C. Second down converter: 750Mhz～1250Mhz L16 connector

Output intermediate frequency: 140Mhz N type/L16 connector

Gain: greater than or equal to 10dB

Tuning step: 1Khz serial port control RS232 standard

Environmental requirements:

LAN and first stage down-conversion meet the working conditions of outdoor unit

The second stage down converter is an indoor unit:

Working temperature: 0 degrees to 35 degrees

Humidity: 10%～100%

Demodulator:

Demodulation method: BPSK, QPSK, UQPSK

Demodulation code rate range: 4 ~ 60Mbps

Input IF: 140Mhz

Carrier capture range: plus or minus 600Khz

Carrier synchronization bandwidth: plus or minus 700Khz

3. GPS time code unit

GPS receiver, complete automatic time calibration function

4. Data intake subsystem

The high-speed data intake board imported from abroad is adopted, and the data intake rate is 400Mbps to realize bit-frame synchronization. Compatible with the intake of various remote sensing satellite data.

5. Receiving and processing platform subsystem

It consists of a workstation and receiving and processing software.

6. Data recording and archiving subsystem

7. High-end data product development and application software subsystem

The system block diagram of the data receiving and fast processing subsystem is shown in Figure 4, and Figure 4 is the system block diagram of the data receiving and fast processing subsystem in the digital earth prototype system provided by the present invention. Most of the hardware of the data receiving and fast processing subsystem has been localized, and it has also partially integrated foreign advanced products, such as high-speed feeder boards. The integration technology of this system has been transformed into high-tech products, and several sets of Earth Observation System/Moderate Resolution Imaging Spectrometer (EOS/MODIS) receiving systems have been developed for the Navy and other units. The operation practice has proved that the system runs stably and the received image is clear. Since MODIS has a spatial resolution of 250 meters, 500 meters, and 1000 meters, a time resolution of at least 4 transits per day, and a spectral range covering 36 channels from visible light to far infrared, it has played an important role in remote sensing applications, atmospheric and ocean research. important role. Compared with similar products, this subsystem has reached the international advanced level. The technical characteristics of this subsystem:

(1) It can receive data from two EOS/MODIS stars, morning star (TERRA) and afternoon star (AUQA);

(2) Adopting two technologies of step tracking and program tracking, the antenna tracking can achieve high precision (≤0.02°), avoiding the problem of signal loss (disconnection) caused by inaccurate satellite attitude and orbit prediction;

(3) Mesh plate antenna is adopted, the wind moment is small, and the antenna gain value is high;

(4) The receiving and processing software has strong functions and fast calculation speed;

(5) The receiving software also has the functions of managing and monitoring the entire ground station, dynamically displays the status parameters of the system operation in real time, and generates operation report files (historical and current);

(6) The 1B data generated by the system is in the EOS/MODIS format of the international general standard, with MOD03 products [Calibrated and geolocated], which are necessary for the development of advanced products;

(7) Generate color snapshot images in real time and automatically generate browsing image files;

(8) Generate a quick-view image in the infrared band at night;

(9) Automatic archiving of source data and product data;

(10) Intelligent management of storage devices such as hard disks;

(11) Automatically read track parameters online;

(12) The whole process of receiving and data processing is fully automated, and the data processing process is equipped with graphic dynamic display.

For the metadata service subsystem, the subsystem designed and partially realized a The metadata service subsystem, its architecture is shown in the figure. It mainly includes the server-side spatial metadata server, the spatial metadata database manager, the client-side spatial metadata query tool, and the spatial metadata management tool. For the convenience of users, the server also provides a spatial metadata gateway, so that users can query and manage the system in a browser through the WWW network.

The metadata service subsystem is divided according to function, including spatial metadata gateway, server-side spatial metadata server, server-side spatial metadata database manager, client-side spatial metadata query tool and client-side spatial metadata management tool.

As shown in Fig. 5, Fig. 5 is a structural block diagram of the metadata service subsystem in the digital earth prototype system provided by the present invention. The spatial metadata gateway is used to realize the information interaction between the client and the server. Generally Z39.50 gateway, using the existing free software. In a sense, it is equivalent to the work of the client's protocol processing transmission module. Its main function is to convert a series of parameters passed by the user through the HTTP protocol into system messages that meet the protocol, then send them to the server, and finally return the results to the browser in HTML format. Here, the Z39.50 protocol is a communication protocol for database retrieval between computers in a client/server environment. Its publication and use solve the problem of data exchange between different systems and overcome the obstacles of information retrieval network ^[10] . It should be mentioned that support for spatial data query was added to the Z39.50 protocol standard after 1992, so that the query of spatial information can be carried out through the Z39.50 protocol.

The spatial metadata server is used to receive the information from the spatial metadata gateway, call the corresponding function module after parsing, and if it needs to return the result set, organize the result and return it in the form of Extensible Markup Language (XML) document To the client, and responsible for the distribution of spatial metadata on the Internet. The release of spatial metadata supports the international common network information search and extraction protocol - Z39.50 protocol, and can be interconnected with other shared systems that support the Z39.50 protocol. Through the spatial metadata server, data users can query the remote spatial metadata database, obtain the spatial metadata set returned by the system, and implement specific operations.

The spatial metadata database manager is used for the collection, storage, management and maintenance of spatial metadata. The storage and management of spatial metadata is mainly undertaken by the spatial metadata database. The data structure of spatial metadata determined in the form of XML documents and the input spatial metadata can establish a reasonable mapping between XML and relational data through the spatial metadata manager, and store them in the relational database. At the same time, with the help of mature relational database software , to realize effective management of spatial metadata, including two aspects of data integrity maintenance and security maintenance. Moreover, the spatial metadata manager is also responsible for establishing a reasonable index to improve high-speed query and retrieval. Of course it is also responsible for adding, deleting and modifying metadata records.

The spatial metadata query tool is used to provide an interface for the client to manage metadata schema information, metadata records and various mapping relationships, including adding, deleting, modifying and browsing metadata records. To use spatial metadata management tools, you need to log in to the spatial metadata server first to obtain certain permissions before performing operations within the permissions

The spatial metadata management tool is composed of a user interface module and a protocol processing and transmission module. The user interface module is used to interact with users, input query conditions and present query results; the protocol processing and transmission module is used to collect query parameters collected by the user interface module Organize query statements, send them to the spatial metadata server through TCP/IP protocol, then query the spatial metadata database, and submit the query results to the user interface module for display.

The main features of the metadata service subsystem are as follows:

(1) In view of the disunity of spatial metadata standards, a reasonable spatial metadata system should support as many spatial metadata standards as possible. The system is based on the FGDC standard and guarantees support for the scalability of the spatial metadata standard.

(2) Since the Z39.50 protocol is currently widely accepted internationally as a communication protocol for database retrieval and query between computers in a client-server environment, the spatial metadata system should provide access to the Z39.50 protocol The interface is guaranteed to respond to any query based on the Z39.50 protocol, so this system supports queries based on the Z39.50 protocol.

(3) The system server provides a gateway, so that users can access the server through the WWW network, because the general metadata query is carried out through the browser. Of course, the metadata management and maintenance interface should also be available through a browser, realizing a true Internet-based spatial information metadata management system.

(4) There is more flexible support for the storage method of metadata, which can be stored by the file system or by the database. Considering the complexity of the internal structure of spatial metadata information, the spatial metadata system can choose XML documents to store the standard templates of spatial metadata, and support the input, storage, query and output of spatial metadata based on XML. In the distributed environment, use XML as a data intermediary to realize the integration and exchange of heterogeneous data. As the next-generation Internet language, XML has great flexibility. Using XML, various complex applications can be launched on the Internet.

The operation process of the system is described as follows: the listening component is established on the server side through the socket. After receiving the connection request from the client, establish a connection with the client. Then the client can send the query request to the server through the Socket connection. The server executes ActiveX Data Objects (ADO) to perform database query operations, and encapsulates the query results in the form of XML, and sends them to the client through the Socket connection. The client gets the required information by parsing the received XML string. This process can be shown in Figure 6 below, which is a schematic diagram of the operation process of the metadata service subsystem in the digital earth prototype system provided by the present invention.

Using XML as the intermediary of data exchange will bring great flexibility to the realization of the system. The system can shield multiple data sources in the background and present it to users with unified XML data. The party receiving the data can process the data arbitrarily according to the "Schema" of the XML data, such as decomposing the data to be processed or presenting it in different styles. Through XML, we can realize the exchange and processing automation of online data, as the basis of the next generation of Internet applications.

The metadata service subsystem includes the following functional modules: administrator login, metadata browsing, you can view all metadata records; metadata entry, complete the entry of metadata attribute information; metadata management, complete metadata query, deletion and attribute information Information modification; account management, the administrator who logs in as a super user will have the authority to create a new administrator account and delete the old account; logout is used to cancel the account.

1. Metadata browsing sub-module

(1) This page mainly completes the function of administrator login. The system will verify the user name and password. If the user name and password match, the verification will pass, otherwise an error will be reported. Test account admin password is power

(2) After successful login, enter the main page of the metadata service subsystem. The left half of the page displays the system functions, and the right half contains the system function introduction page.

(3) The metadata browsing page lists all the records in the database of the metadata management system, and displays the Chinese dataset name, English dataset name and summary of each record. Click the Chinese dataset name, and the dataset Chinese name will be displayed The details of the corresponding corresponding metadata record. You can also view the detailed information of the corresponding metadata record in XML form.

2. Metadata entry sub-module

(1) The metadata input sub-module lists the fields that should be filled in to add metadata records, and there is a button at the bottom of the page that prompts "Enter the input and start storing". Verification is required before starting storage. The error checking mechanism of this page: After clicking the button, activate the error checking function, check whether the required fields are empty, and there will be corresponding prompts. For all fields that do not meet the requirements, a prompt will be given to re-enter the correct content.

(2) Display all the information just entered for verification by the user. If you make changes, you can return to the previous page, otherwise continue to click the button "Confirm and upload officially".

(3) The page officially adds records to the database, and writes the information just filled in to the corresponding fields in sequence. This process is invisible, reminding the user that the new record has been added. And there is a button to close the page. End all operations of adding new records this time.

3. Metadata management sub-module

(1) The query system is mainly to help managers quickly locate the metadata records to be searched, so as to verify the field content, or change, or even delete the entire record. The search fields included in the metadata query are: dataset Chinese name, data start date, data end date, maximum longitude, maximum latitude, minimum longitude, minimum latitude, keywords. All search fields are fuzzy queries, and the relationship between all fields is "and". Empty fields will be automatically masked and no matching search will be performed. When each field is blank, all metadata records in the database will be listed.

(2) Return query results according to specific query conditions, and list the Chinese data set name, English data set name and abstract of each record that is queried. Click on the name of the Chinese dataset, and the detailed information of the corresponding metadata record corresponding to the Chinese name of the dataset will be displayed. You can also view the detailed information of the corresponding metadata record in XML form. You can also view the detailed information of the corresponding metadata record in XML form. There are two links "Edit Information" and "Delete Record" at the bottom of the page.

(3) According to the specific information of the records displayed in the Chinese name of the data set, you can directly modify the key parts. There are two buttons at the bottom of the page to perform the modification operation and the abandonment operation respectively. Update relevant fields according to the transmitted information, and remind users that the modification of metadata record attributes has been completed. And there is a button to close the page. End all operations of modifying the record this time.

(4) If you choose to delete the record, the system will delete the record and prompt the user that the record has been deleted.

4. Account management sub-module

This page retrieves the account records of ordinary administrators in the database. If the number of records is 0, it will display "No other login account has been established yet"; ). At the bottom of the page, there is a functional area for creating a new account, enter the user name and password. In order to remind the operator of case-sensitive issues, a password verification step has been added. Although the password will be displayed in plain text, only the second link is used as a reminder.

Each account information column contains a "delete" action link. After clicking, the user is asked to confirm whether to delete the account, and after confirmation, the deletion operation will be executed and it will automatically switch back to the initial page of account management.

For the model library subsystem, the general models stored in it include at least: improved homomorphic filter cloud removal model, pyramid structure remote sensing data fast compression and playback model, multi-scale remote sensing image wavelet fusion model, hyperspectral remote sensing image cube model, surface temperature Inversion model, spline multi-scale image representation and registration model, radar data soil moisture inversion model, vegetation index model biomass model, net primary productivity model, microwave remote sensing data ground parameter inversion model and radar interferometry (INSAR ) model, etc.

Model is the intermediate process of processing raw data into usable information and knowledge, and it is an important content of Digital Earth application. The Model Library of Digital Earth Version 1.0 includes models independently developed by the Institute of Remote Sensing in recent years, including 16 models such as image preprocessing, inversion of ground material properties, data fusion, and microwave data processing. These models have been verified and published at home and abroad on publications. Some models that have been used for many years, such as vegetation index, are not included in this model library. This model library will continue to include new models, and at the same time hope to use and improve the models that have been included. The model library is introduced to users in the form of model name, algorithm and document description.

1. Improved homomorphic filtering cloud removal model

The image function f(x, y) can be simplified as the product of the light source incidence function fi(x, y) and the ground reflectance function fr(x, y). Here, some changes are made to the form of formula (1):

f(x,y)=i ₀ ×f _i (x,y)×f _r (x,y)

Among them, i0 is the light source incident constant, which is constant for every point (x, y) on the surface, fi(x, y) is the incident quantity change function caused by thin clouds, fr(x, y) is Ground albedo function.

fi(x, y) mainly reflects the low-frequency part in the image, that is, the brightness difference in a large range. The problem of cloud removal is transformed into how to reduce the uneven illumination of the image caused by fi(x, y), and the average brightness of each part of the image fluctuates, making it difficult to distinguish the detailed structure of the image corresponding to the cloud area.

Log transformation:

Since fi(x, y) and fr(x, y) are product relations, they cannot be processed separately in the frequency domain. Therefore, taking the logarithm for (l') is to transform the product relationship in the space domain into an additive relationship:

g(x,y)=lnf(x,y)=lni ₀ +lnf _i (x,y)+lnf _r (x,y)

In practice, it is necessary to add 1 to f(x, y) and then take the logarithm. This is to avoid taking the logarithm of 0 and getting a meaningless value.

Low-pass filtering:

If the high-pass filter is directly used to extract fr(x, y), the details of the cloud-free area will be unnecessarily over-enhanced, causing image distortion. Since fi(x, y) changes slowly, and fr(x, y) changes rapidly, by performing low-pass filtering on g(x, y), fi(x, y) can be separated first.

g'(x,y)=LPF(g(x,y))≈lni ₀ +lnf _i (x,y)

If an image contains noise, according to the statistical characteristics of noise interference, it can be assumed that these noises are irrelevant to each coordinate point, and their mathematical expectation value is zero. Similar to noise, ln(fr(x, y)) changes rapidly, and it can also be assumed to be uncorrelated with respect to each coordinate point (its mathematical expectation value is not zero). For a certain area of the image, ln(fr(x, y)) can be regarded as "random additive noise", which can be "eliminated" after averaging the area. [8] In order to simplify the calculation, the neighborhood average method is used to approximate the low-pass filter operator:

${\mathrm{<span\; class="notranslate">\; g</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; LPF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&ap;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; N</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ]</span>}{\overset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ]</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ]</span>}{\overset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ]</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

Wherein, N is an odd number, and the above calculation is performed in an area of N×N size centered on (x, y). [N/2] represents the integer part of N/2. The value of N should be appropriately large, otherwise, for the entire image, a small neighborhood average cannot weaken the high-frequency part well.

2. Pyramid structure remote sensing data fast compression and playback model

Visualization is realized through the window through which the digital earth interacts with people. Reconstruction of 3D digital earth with remote sensing digital images is the key technology and most notable feature of this study. Seamlessly splicing remote sensing images of different scales and even the entire earth, country and region, roaming and zooming in at will, and constructing a virtual stereo from 3D data through the method of artificial parallax. The key technology to realize the above tasks is the fast compression and playback technology of wavelet different scale remote sensing data pyramid. In the research, the rapid compression and playback software of wavelet different scale remote sensing data pyramids was developed. The principle is as follows:

In order to store more images with limited storage capacity, it is necessary to study how to compress the image data to the maximum extent, and ensure that the image quality and speed reconstructed by these compressed data are acceptable to users. This is the larger image The problem to be solved by stepless display and compression.

Since there is a close correlation between image pixels, that is, the image data has a high degree of redundancy, the image data can be compressed. On the other hand, for reconstructing the image, there must be at least four quantities of amplitude, phase, and two spatial frequencies. These four variables are independent of each other, that is, any one of them cannot be obtained from the other three variables. Therefore, image data compression can be completed by converting an image containing a large number of related pixels into a data group consisting of only non-related elements through some reversible transformation (compression-decompression). At present, the commonly used methods include data block (tile), pyramid level image and wavelet compression. On the one hand, image compression is performed to reduce redundancy; on the other hand, the storage structure is optimized to increase the speed of image decompression. The data block technology is suitable for continuous overlapping of small images to form a large image display, which is mainly used in the field of game map display. Pyramid-level image compression technology is an important direction of current research.

Wavelet compression is the latest video compression technology at present, which can realize high-quality picture transmission, compression and storage at a lower bandwidth. At the same time, wavelet is a rapidly developing new field in current mathematics. Broad double meaning. Compared with Fourier transform, windowed Fourier transform or Gabor transform, it is a local transformation of time and frequency, so it can effectively extract information from the signal, and it can be processed by operations such as stretching and translation. Multiscale analysis of functions or signals solves many difficult problems that cannot be solved by Fourier transform, so wavelet transformation is known as "mathematical microscope", which is a milestone in the history of harmonic analysis. Wavelet analysis can analyze the signal components in the specified frequency band and time (space) domain. Time (space) frequency domain localization properties and multi-resolution analysis features are the main characteristics and advantages of wavelet analysis. It can observe the subtle features of the signal at any time (space) scale and at any high resolution.

Defined in the time (space) domain:

is the effective bandwidth of the wavelet function ψ(x). Therefore, the time width center of the telescopic translation wavelet ψs(x-x0) is x0, and the effective time width is sσx. In the scale-space plane, by

[x ₀ -sσ _x , x ₀ +sσ _x ][ω ₀ /s-σ ₀ /s, ω ₀ /s+σ ₀ /s]

The defined rectangular window reflects the time (space) frequency domain resolution of the wavelet transform. When the scale s increases, the wavelet filter center frequency decreases, the frequency band narrows, and the time window width increases. This means that in the low frequency band, there is an increasingly higher frequency resolution. And vice versa, which means that the high frequency band will have better time (position) resolution.

In the process of compression, the pyramid level is used to store wavelet coefficients of different resolution levels, different code word lengths are used for encoding, and unnecessary wavelet coefficients are compressed according to different requirements of resolution, so as to achieve the purpose of encoding and compression. In the software, first read the file header information of the image image to obtain the size of the data and the size of the pixels;

Secondly, according to the coordinates of the four corners of the screen in the mapped memory, calculate the scale and coordinates of the required image; decompress the data according to the different requirements of the resolution; transfer the data to the mapped memory. Finally, the mapped memory is reversed into display memory for display to the user.

The compression ratio of remote sensing data is greater than 20%, and the playback time is within 3 seconds.

3. Multi-scale remote sensing image wavelet fusion model

The information difference (that is, the details) at different resolutions can be obtained by decomposing the function on a small wavelet orthogonal basis, which solves the details of the previous images defined on the increasing resolution sequence (V ^j ) ^j∈Z The correlation problem between the details that is difficult to solve when describing, removes the redundancy of information.

If a closed subspace {V _j } _j∈Z in the square integrable function space L ² (R) satisfies certain conditions, then {V _j } _j∈Z is called a multiresolution analysis of L ² (R) ( MRA). We can obtain the orthogonal decomposition of space L ² (R) by using multi-resolution analysis.

Let W _j+1 be the orthogonal complement space of V _j+1 on V _j , then we have $V_j=V_{j+1}\&CirclePlus;W_{j+1}$

For f∈L ² (R), it is known that the approximation of f(x) at 2 ^j+1 and 2 ^j resolutions is equal to its orthogonal projection in V _j+1 and V _j respectively, then f(x) The high frequency detail at 2 ^j resolution is an orthographic projection in the space W _j+1 .

Let the projections of f(x) in V _k and W _k at 2 ^k (k∈Z) resolution be f _k ∈ V _k and g _k ∈ _{W k} , then f _k ＝ f _k-1 + g _{k -1} , so

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="f"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">f</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="g"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">g</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="g"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">g</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="g"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">g</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

是W_k的规范正交基，因而f_k，g_k可表示为：Since {φ _{k, n} : k, n ∈ Z} is an orthonormal basis of V _k ,

is the orthonormal basis of W _k , so f _k , g _k can be expressed as:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\mathrm{<span\; class="notranslate">\; \&phi;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; l</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

称为小波函数。where φ is called the scaling function,

called the wavelet function.

Decomposition algorithm: find {c _{n k} ^-1 } and {d _n ^k-1 } from {c n ^k }

$\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\\ {\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\end{array}\right.$

Reconstruction algorithm: find {c _n ^k } from {c _n ^k-1 } and {d _n ^k-1 }

${\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; l</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; l</span>}}{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ]</span>$

The above decomposition algorithm and reconstruction algorithm are also called Mallat algorithm. Among them, {a _n }, {b _n } are called decomposition sequences, and {p _n }, {q _n } are called reconstruction sequences.

4. Hyperspectral remote sensing image cube model

Hyperspectral is high spectral resolution remote sensing to obtain continuous ground object spectral images in a specific spectral domain, and hyperspectral remote sensing applications can be expanded in the spectral dimension. The pixels in the hyperspectral image are composed of continuous bands. One way to express the relationship between the spectrum and the surface phenomenon is to build an image cube, as shown in the figure below.

In the image cube, the x and y axes represent the spatial dimensions of the spectrum, and a farm is represented in the figure; the z axis consists of all remaining bands, superimposed like pages in a book.

Image Cube Maker:

(1) The top-level image is a composite image of R, G, and B;

(2) The z-axis flow change of the color along the edge pixel is represented by R, G, B;

(3) The z-axis direction of all pixels constitutes a spectral cube, which contains a lot of change information.

5. Surface temperature inversion model

Improved split-window algorithm for surface temperature inversion: Established a series of model coefficients for split-window algorithms affected by different observation angles and surface elevations. (1) Considering the influence of atmospheric radiation paths when different satellite observation angles are considered. The following intervals were established between 0° and 65°: 10° intervals between 0° and 30°, and 5° intervals between 30° and 65°; (2) Using the typical surface spectrum knowledge base and vegetation The canopy radiative transfer model establishes the relationship between leaf area index and pixel specific emissivity, and uses this relationship as prior knowledge to provide pixel-level specific emissivity values for surface temperature inversion; (3) according to my country's terrain Obvious 3-level step difference, establish algorithm model coefficients for different altitudes,

${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}{\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}\frac{\mathrm{<span\; class="notranslate">\; \&Delta;\&epsiv;</span>}}{{\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="B"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">B</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}{\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}\frac{\mathrm{<span\; class="notranslate">\; \&Delta;\&epsiv;</span>}}{{\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 31</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 32</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; C</span>}$

Realize the day and night algorithm for surface temperature inversion: establish a lookup table between the radiance values of the 7 channels of the MODIS mid-infrared and thermal infrared windows and each parameter, and use the lookup table to obtain the initial atmospheric profile closest to the given observation value Atmospheric profile; the positive mode uses RTTOV7.0 mode to calculate the radiance value under different atmospheric conditions; the inversion algorithm uses regularization constraints and Newton iterative inversion algorithm.

6. B-spline multi-scale image representation and registration model

Based on B-spline multi-scale representation has multi-resolution characteristics, the first derivative and Hessian matrix can use the characteristics of multi-scale recursive calculation to achieve image matching, the basic model,

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="S"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">S</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; g</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&infin;</span>}{\overset{<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&infin;</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="R"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">R</figure-callout></span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}$

${\mathrm{<span\; class="notranslate">\; \&beta;</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{{<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; j</span>}}}{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; !</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>}_{<span\; class="notranslate">\; +</span>}^{\mathrm{<span\; class="notranslate">\; no</span>}}$

in, ${\left(x\right)}_{+}^{\mathrm{no}}=\max {\left(\mathrm{0,1}\right)}^{\mathrm{no}},\mathrm{no}>0$

B-spline derivative recursive calculation model,

$\frac{<span\; class="notranslate">\; \&PartialD;</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&PartialD;</span>\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>$

7. Inversion model of soil moisture from radar data

Using wolfgang's model based on dynamic detection, it is considered that the comparison between the backscatter coefficients (normalized to 40°) of the same point can obtain the soil moisture information of the point. For the same point, the largest and smallest σ°(40) represent the driest and wettest surface states, respectively σ° _dry (40, t) and σ° _wet (40), where σ° _dry (40, t The t in ) indicates that the driest conditions vary with the seasons.

Assuming that σ°(40) has a linear relationship with surface soil moisture, Wolfgang’s soil moisture acquisition model is expressed as:

8. Vegetation index

Ratio vegetation index:

NDVI=(TM4/TM3)

Normalized Difference Vegetation Index:

NDVI=(TM4-TM3)/(TM4+TM3)

Soil Adjusted Vegetation Index:

SAVI=[(I+L)*(TM4-TM3)]/(TM4+TM3+L), L=0.5

Convert vegetation index:

TVI＝(NDVI+0.5)

Environmental Vegetation Index:

EVI=TM4-TM3

9. Biomass model

Vegetation index cannot be directly used to measure biomass or NPP, it estimates these biological parameters through the relationship with leaf area index (LAI), etc. The leaf surface absorbs photosynthetically active radiation, photosynthesizes organic matter, and then links vegetation index with biomass through LAI. S.W Todd et al. used TM data in 1998 to study the grazing and non-grazing shortgrass grasslands located in the rain shadow area of the Rocky Mountains. The correlation coefficient between the NDVI of the grazing area and the aboveground biomass was 0.812, and the NDVI of the non-grazing area was related to the biomass. The biomass correlation coefficient is 0.304, and a spectral model of pastoral biomass is established,

Biomass=60.86+352.71*NDVI, where R2 is 0.66.

10. Net Primary Productivity Model

NPP is the carbon flux from the atmosphere into green plants per unit time, and NPP is the basic element to measure the change of ecological environment.

Net primary productivity (NPP) general formula: NPP＝0.29*[exp(-0.216)*RDI**2]Rn

Rn: annual net radiation (kcal/cm**2)

RDI: Radiation dryness index (=Rn/(lr))

I: Potential heat of transpiration (580cal/gH20)

R: annual precipitation (cm).

11. Inversion model of ground parameters of microwave remote sensing data

The Gaussian IEM surface scattering model is applicable to $\left(\mathrm{ks}\right)\left(\mathrm{kl}\right)<{1.2}^{\sqrt{\mathrm{\&epsiv;}}},$ It has a wide range and is used as the basic formula for ground parameter extraction.

${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; pp</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; pp</span>}}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span>\frac{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}}{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; pp</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; pp</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ]</span>$

Among them, pp represents the polarization state of incident-scattering, σ _pp represents the ground scattering coefficient in this polarization state, s _r is the ground roughness parameter s _r =(ks) ² W related to the ground discrete height S and the correlation length L. W is the first-order energy spectrum of the Gaussian distribution ground correlation function.

Roughness: There are two parameters to describe the roughness of the ground, the ground discrete height s and the ground correlation function. For one-dimensional discrete data, the pixel unit ground discrete height is,

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>\sqrt{\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; N</span>}{<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}$

The ground roughness parameter S _r related to the ground discrete height s and the ground length L is related to the backscatter coefficient:

$\mathrm{<span\; class="notranslate">\; 10</span>}\mathrm{<span\; class="notranslate">\; log</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; vv</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; vv</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; vv</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; 10</span>}\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="log"\; filenames="C20071011801200611.png"\; state="\{\{state\}\}">log</figure-callout></span>}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{\mathrm{<span\; class="notranslate">\; r</span>}}}$

12. Radar interferometry (INSAR) model

Synthetic Aperture Radar Interferometry (INSAR) uses two Synthetic Aperture Radar (SAR) images in the same area as the basic processing data. By calculating the phase difference of the two SAR images, the interference image is obtained, and then unwrapped by phase, from A new technology for obtaining terrain elevation data in interference fringes, a basic model of interferometry,

${\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; B</span>}\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; B</span>}}_{<span\; class="notranslate">\; \&perp;</span>\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; 0</span>}}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; lp</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; 0</span>}}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; )</span>$

B-baseline distance;

${\mathrm{<span\; class="notranslate">\; B</span>}}_{<span\; class="notranslate">\; \&perp;</span>\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; B</span>}\mathrm{<span\; class="notranslate">\; cos</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

H _p = terrain height;

D _p = surface movement;

α = baseline horizontal angle.

Differential radar interferometry (D-INSAR): Differential interferometric radar measurement (D-INSAR) refers to the use of two interferometric images of the same area, one of which is the interferometric image acquired by two SARs before the deformation event, and the other The first one is the interference image obtained by two SAR images before and after the deformation event, and then the difference processing of the two interferograms (removing the influence of the earth's curved surface and terrain fluctuations) is a measurement technique for obtaining the micro-deformation of the surface.

For the spatial information database subsystem, in order to meet the massive data requirements of the digital earth prototype, the remote sensing data subsystem takes the deep processing of multi-scale remote sensing standard data as the main content. Including spatial data processing, remote sensing data processing, feature information processing, thematic information analysis and other four main modules.

The spatial data processing module is used to implement control bottom data processing, bottom control framework layout and bottom data accuracy inspection. The spatial data processing module establishes the preprocessing link of the remote sensing standard data platform, uses the topographic map control background conforming to the national surveying and mapping standard for the execution process of the mathematical model of the geometric fine correction of the multi-temporal and spatial scale remote sensing data, and generates it for the quality and accuracy inspection of the geometric fine correction The reference background, so that the measurable inspection has evidence to rely on. mainly include:

1. Control the underlying data processing - realize the digital raster map in which the scanned topographic map conforms to the theoretical value through geometric fine correction, and eliminate the scanning error and the deformation error of the paper topographic map. It is the basis for the realization of the mathematical model of the geometric fine correction of remote sensing images; it is also applied to the basic unit of the reference background for the quantitative inspection of data products.

2. The layout of the underlying control framework - with the digital grid map as the basic unit, a basic control framework that satisfies various coordinate systems is formed, which is the basis for the realization of the mathematical model of remote sensing image geometric fine correction, and also has the ability to test the quality and accuracy of image data products Refer to the function of the background.

3. Underlying data accuracy inspection——it is the reference background inspection of the basis for the realization of the mathematical model of geometric fine correction of remote sensing images and the quality accuracy of related data products, so as to ensure the authority of the reference background.

The remote sensing data processing module is used to realize the establishment of the control point library with the same name, the measurable precision processing inspection and the establishment of the image metadata database. The remote sensing data processing module establishes a remote sensing standard data platform to uniformly locate and calibrate remote sensing data of various temporal and spatial scales, so that various remote sensing image data can be normalized into the same coordinate system, and the concept of quantification runs through each In the processing link, the quality and accuracy inspection as the basic data of remote sensing applications is established in a measurable environment, and standard remote sensing data backgrounds suitable for various application purposes are generated. mainly include:

1. Establishment of the control point library with the same name——Select the ground control point set that can meet the requirements of the mathematical model for precise geometric correction and has a typical meaning. The rationality of its selection is directly related to the composite use accuracy of different data sources and the basis of dynamic comparative analysis of data in the same area. It is the core to ensure the accuracy of the remote sensing image data product system, and prevent the transmission of errors to the greatest extent. And the proposed "control point technology of the same name" is used to control the system accuracy of geometric fine correction of remote sensing image data, so as to prevent the systematic error of remote sensing image products caused by different control point selection schemes. Its main function is to make the measurable inspection of the quality of remote sensing image data products have practical significance.

2. Measurable precision processing inspection - inspection of the quality accuracy of remote sensing image geometric fine correction products, since the image data product and the reference background of the inspection are placed in the same inspection environment, the quality inspection is free in a transparent state In this way, the previous ambiguous expression method of using a region pixel value for the image accuracy of each scene is abandoned, which ensures the authority of the test results.

3. Establishment of image metadata database - including remote sensing image description information, geometric fine correction control point selection information and standard image product inspection reports, etc. It is an important symbol to measure the standardization level of the entire remote sensing image processing process. And make the context of each specific link of data product generation tend to be clear, so that the test results of quality accuracy are convincing.

The feature information processing module is used to restore and enhance color, image mosaic color matching, non-homologous data assimilation and multi-scale data fusion. The feature information processing module objectively extracts the inherent feature information in various spectral band matching responses based on the standard remote sensing data background, standardizes the operation process of information extraction, and forms a mature characteristic algorithm model. Try new information extraction algorithms; further explore and improve the standards for remote sensing image mapping and thematic mapping. mainly include:

1. Color reduction and enhancement

2. Image mosaic color matching

3. Non-homologous data assimilation

4. Multi-scale data fusion

The thematic information analysis module is used to implement various remote sensing application analysis based on the standard remote sensing data platform for remote sensing data results. The thematic information analysis module conducts various remote sensing application analysis based on the standard remote sensing data platform based on the standardized remote sensing data results according to the key environmental issues of national concern. It aims to highlight the application of interactive interpretation results reflected by the inherent information of remote sensing; pay attention to the intersection and penetration between disciplines and fields, and gradually form remote sensing analysis methods applied to different disciplines and fields. Provide sustainable and various scientific analysis data to relevant national departments. At the same time, combined with the characteristics of multi-field coverage of remote sensing data, we can understand and grasp the degree and trend of remote sensing data application needs in various fields in a targeted and timely manner. In terms of urban environment, vegetation dynamics, water dynamics, cultivated land dynamics, and desertification, targeted application analysis based on standard data platforms has been carried out. Work will also be carried out on wetland environment, coastal zone changes, and remote sensing archaeology.

For the grid computing subsystem, the digital earth is a virtual earth constructed by massive, multi-resolution, multi-temporal, multi-type space earth observation data and socio-economic data and its analysis algorithms and models. The processing of remote sensing data is a key in Digital Earth. The grid computing subsystem is a processing platform for remote sensing data based on grid technology. At present, we have realized the prototype system of the grid computing subsystem. This system uses MODIS data obtained remotely from a third party to perform inversion of ground and atmospheric parameters, and can also provide information on atmospheric aerosol thickness and vegetation index in a specified geographic area. The system provides a JSP Web portal, and users use the system through a browser (IE). The system obtains data from remote data servers or locally according to user needs, triggers services and monitors their running status. During task execution, the system divides a task into several subtasks, then assigns the subtasks to PCs (or high-performance computers) in the grid computing pool for execution, recycles and integrates the execution results, and returns the results to the user .

The logical structure of the grid computing subsystem and external modules is shown in Figure 7, which is a schematic diagram of the logical structure of the grid computing subsystem and external modules in the digital earth prototype system provided by the present invention. The grid computing subsystem is related to the following two external modules:

(1) Data server: The grid subsystem only provides demonstration data locally, and other data must be obtained from a third-party data server.

(2) High-performance computer: When some processing tasks are too large and the grid subsystem is difficult to handle, the task is handed over to a high-performance computer for processing.

The logical structure of the grid computing subsystem and internal modules is shown in Figure 8, which is a schematic diagram of the logical structure of the grid computing subsystem and internal modules in the digital earth prototype system provided by the present invention.

In addition, FIG. 9 and FIG. 10 also respectively show a schematic diagram of a data flow structure of the grid computing subsystem and a schematic diagram of a control flow structure of the grid computing subsystem.

The main modules (module functions, module interfaces) of the grid computing subsystem are as follows:

1. Process control module

Function: Process control in units of applications. Adopt workflow technology.

Interface: interact with the JSP website. The input is the remote data URL, the parameters required by the application (filled in by the user), and the order number (generated by the website).

2. Grid middleware

Function: Centralized management and scheduling of underlying hardware computing resources. Responsible for distributing the tasks submitted by the upper layer to available computers for calculation according to a certain strategy, and retrieving the results.

Interface: take batch files and task description files as carriers. The input is the executable program of the task, the input and output parameters, and the operating environment requirements; the output is the calculation result and local log information of the task.

3. Initialize the module

Functions: Prepare for special processing, including data acquisition, log file creation, remote data acquisition, decompression, subtask segmentation and packaging, data format conversion and interface conversion.

Interface: in XML format, input parameters are remote data URL, remote sensing parameters required for special processing, task order number; output parameters are subtask package parameters.

4. Thematic processing module

Functions: The node provides surface temperature inversion, aerosol optical depth inversion, a series of vegetation indices (NDVI, EVI, SAVI), land cover classification, cloud detection, geometric correction, clipping, splicing, and matching functions. For specific function descriptions, see SIG-SID-PS-001 to SIG-SID-PS-011.

Interface: using a private protocol, the input parameters are subtask package parameters; the output is ASCII format data processed by special functions, latitude and longitude data, and metadata.

5. Post-processing module

Function: Merge the calculation results of subtasks, generate HDF files, compress them, and put them into the result database.

Interface: The input is the result file set of the subtask (ASCIII format data, latitude and longitude data, metadata); the output is the URL of the final compressed package result.

The current specific service functions of the grid computing subsystem are:

1. Surface temperature retrieval function

Function description: Through the method of moving the window and adaptively splitting the window, the inversion of the surface temperature is realized by using the thermal infrared data of MODIS.

2. Aerosol optical depth retrieval function

Function description: Use the dark pixel method to extract the aerosol optical depth parameters of MODIS images.

3. Normalized Difference Vegetation Index (NDVI)

Functional description: firstly perform geometric correction on MODIS data, and then generate NDVI from single data.

4. Enhanced Vegetation Index (EVI)

Functional description: firstly perform geometric correction on MODIS data, and then generate EVI from single data.

5. Soil Adjusted Vegetation Index (SAVI)

Functional description: firstly perform geometric correction on MODIS data, and then generate SAVI from single data.

6. Land cover classification

Function description: First, geometrically correct the MODIS data, and then use the multi-band information of remote sensing images to perform non-supervised classification to achieve the purpose of extracting land cover information.

7. Cloud Detection

Function description: Firstly, geometrically correct the MODIS data, and then filter out the clouds in the image.

8. Geometry Correction

Function description: Use the latitude and longitude information of 1KM resolution in the 1B data of MODIS 500m resolution as control points to perform geometric correction on the MODIS data with 1KM resolution, and the gray scale resampling method is the nearest interpolation method.

9. Image cropping

Functional description: First, geometrically correct the MODIS data, and then cut out a smaller research area from the large image.

10. Image Stitching

Functional description: First, geometrically correct the MODIS data, and then splice multiple adjacent MODIS 1B data images into a larger complete image through the geographic coordinate data inside the MODIS data.

11. Image matching

Functional description: first perform geometric correction on the MODIS data, and then acquire the data of the same area through the positioning information of each pixel obtained from the result of the geometric correction, and use it to generate matching multiple images of the same area.

For the map application service subsystem, the subsystem manages spatial data and attribute data through node-based attribute binding, hierarchical management technology and R-tree technology, and adopts skin + skeleton technology, with the help of distributed storage and distributed computing The technology realizes the storage and dynamic loading and display of massive data, including at least the visualization module and the query module, which are the information windows of the prototype system platform for external services.

The map service subsystem is a global visual information system based on the Internet environment. The system infrastructure supports streaming operations based on packaged devices to transmit continuously updated data, and is formulated as a packaged dynamic service page (turnkey ASP) solution. Third-party data can be released independently and integrated with the main data fed back by the client.

In response to the current requirements of remote sensing technology for massive data storage and transmission capabilities, it integrates two of the most advanced wavelet-based image compression technologies in the world: multi-resolution image wavelet compression format (MrSID) and enhanced compression wavelet (ECW); provide It has the ability to compress and manage massive image data, and solve the real-time and fast transmission of large-scale terrain data on the Internet.

The system manages spatial data and attribute data through node-based attribute binding, hierarchical management technology and R-tree technology. In addition, the skin + skeleton technology is adopted, and the problems of massive data storage, dynamic loading and display are solved with the help of distributed storage and distributed computing technology.

The use of TB server cluster technology solves the bottleneck problem of Internet GIS and ensures the efficient operation of GIS applications with large data volumes. When the scale expands, it only needs to add application instances or servers, and the originally developed applications do not need to be changed, and can be added or uninstalled dynamically without interrupting the service. While reducing costs, greatly improve system performance.

The client adopts the technology of asynchronous transmission and client caching, while transmitting and decompressing the data, and uses M-Band Wavelet to simplify the terrain according to the position of the viewpoint, so as to solve the problem of users waiting for a long time.

On the client's browser, vector data, labels and building models can be superimposed on the terrain layer. The system integrates global terrain scene browsing, spatial analysis and two-way query.

The system adopts the network real-name plug-in (ActiveX) control to build a network client/server structure (Client/Server) model, as shown in Figure 11, which is a schematic diagram of the service flow of the map application service subsystem in the digital earth prototype system provided by the present invention. ActiveX is an Internet technology based on Object Linking and Embedding (OLE) technology. Its foundation is COM (Component Object Model). It is not only an automation object, but also a standard COM object. used by programming languages or application systems. The combination of ActiveX control and Web browser has fast execution speed, can be implemented in multiple languages, and can reuse the source code of the original software, thereby improving the efficiency of software development and quickly establishing a full-featured application system. The ActiveX control can be realized through the development graphics language (OpenGL) graphics library and ATL template. The interpretation model of the OpenGL command is the client/server model, that is, the client (the application program that uses OpenGL for drawing work) issues commands to the server. These OpenGL commands It is interpreted by the server, so OpenGL has network transparency; ATL (ActiveX Template Library) has good support for Microsoft's various new component technologies based on COM, such as MTS, ASP, etc., and it can quickly develop efficient, Concise code, providing maximum automatic code generation and visualization support for the development of COM components. The digital earth virtual system based on ActiveX controls relies on ActiveX to complete the modeling and display of 3D virtual terrain landscape.

The client part is the interface and interface for interacting with remote users, and communicates with the server through the http protocol. It mainly completes the modeling and display of 3D terrain landscape, scene roaming and operation, spatial analysis and attribute information query, 3D model interface and other functions.

The client buffers the data of different scales separately. When the user operates, it first checks whether the data in the buffer meets the requirements. If not, it requests the supplementary data from the server. The client transfers the server's DEM data and image data to the local machine can be divided into two processes: one is when the ActiveX control is started on the browser, the index file of the data block is transmitted to the client and read into the memory; the other is the viewpoint Real-time data transmission when changing or roaming: As the viewpoint is drawn toward the ground, the viewing angle becomes smaller accordingly, and the client then requests data with higher resolution and smaller spatial range from the server. When the server is called in, no more requests will be sent; when the terrain is roaming along the specified route, the data that has not been called in before but is located in the viewing volume must be drawn in real time. In order to ensure the continuity of data transmission, the client program adopts a multi-threaded mode. When a certain layer of data is displayed, another thread transfers its adjacent data blocks in the background at the same time.

The main features of the map application service subsystem are: (1) Through appropriate indexing and retrieval parameters, different levels of data can be transmitted in real time, ensuring data consistency and integrity, and realizing data reuse at the client. (2) Once the data is loaded into the memory or resides locally, it is convenient for the server side to be irrelevant, so it can reduce the redundant transmission of data and reduce the pressure on the server. These features can meet the requirements of mass data visualization for multi-layer information display, effective data transmission, intelligent scaling and other technologies.

One of the characteristics of the Web environment is the quick response to user operations. There are a lot of information to choose from in the Internet. If the waiting time is too long, the user will give up the request for this information or transfer to other information services. The server side should reduce the amount of calculation as much as possible, and distribute more data calculations to the client. The server side includes two parts: one is the Web server part, and the other is the database part. The web server is mainly to receive the request submitted by the client, quickly search for the data that meets the conditions in the database, and return the result to the remote client through the HTTP protocol. The database of virtual reality system oriented to Digital Earth mainly includes 3D model database, digital elevation model (DEM) database and image database. Its data ranges from DEM data with a resolution of a few minutes or seconds covering the whole world and satellite images with a resolution of hundreds of meters to high-resolution DEM data and orthophoto aerial images in local areas. The amount of data can be imagined, ranging from a few A G, as many as hundreds of G or even several TB. For such a large amount of data, in order to achieve the purpose of real-time interaction on the medium and low-end microcomputer, the data must be classified and managed in blocks.

The functional modules of the map application service subsystem mainly include:

1. Visualization module

After the system is started, the map can be controlled through menu commands, and can be enlarged steplessly. As the scale zooms in, larger resolution imagery within the display area is recalled. Be able to observe from different angles, browse according to the prescribed route, etc. It can realize from a global overview to a detailed observation of a town or even a smaller area, and can roam any area of the world through a keyboard, mouse or navigation ball, truly realizing "what you see is what you get".

2. Query module

(1) Basic functions of "data query of basic spatial data":

Zoom in, zoom out, roam and reset the map.

Layer control, browser-side vector format display.

Support for concurrent users

query graph from information, query information from graph

Selected primitives flicker

eagle eye

Represents multi-layer data (various topics, classification of basic vector data, such as roads (roads, streets, railways, notes), water systems, green spaces, borders, overpasses, bus lines, underground pipelines, hospitals, schools, post offices, refueling Stations, toll booths, parking lots, telephone booths, railway stations, sports venues, cinemas, art galleries, memorial halls, museums, exhibition halls, libraries, former residences, department stores, supermarkets, shopping malls, office buildings, office buildings, commercial buildings, mansions, banks Equal distribution) Equal layer switch display and disappear effect

Indicates the query location (administrative place name, natural feature name, building name, street name, hospital, school, post office, tourist attraction, restaurant, etc.) of the place name database with attribute data.

(2) Graphic display

The system can comprehensively display various levels of vector graphics and basic topographic maps, including stepless zooming of graphics, smooth roaming of graphics, and "Eagle Eye" function of graphics display.

(3) Spatial query

Flexible and diverse two-way spatial query can be performed on the graphic information and attribute information (database information) in the GIS system.

For the virtual reality subsystem, combined with the results of other subsystems of the digital earth prototype system, the virtual reality subsystem mainly develops the following functional modules.

1. Mass data management function module

Because some industry departments not only need to manage the data collected in real time, but also need to retain and organize historical data for a period of time in order to predict the relevant situation.

The system platform has massive data management functions through the establishment of a simulation-specific geographical landscape database and a simulation-specific thematic database.

At the same time, the software and hardware of the system adopt international leading products, which have the characteristics of not crashing for different types and scales of data and objects of use, as well as a flexible and powerful recovery mechanism; it has a complete authority control mechanism to ensure that the system is not intentionally or illegally accessed. Unintentional destruction; capable of ensuring data security and consistency in an environment of concurrent response and interactive operations.

2. Real-time roaming browsing and editing function modules of large geographical landscapes

Support multi-angle viewing and full-screen browsing. A variety of browsing control methods can be switched freely.

Support historical playback. The browsing process can be recorded and played back when needed.

Supports screen grabbing. The current screen content can be saved as an image file at any time.

Flexible and convenient flight path editing function. With the flight path editor, you can edit the flight path as you like.

Support target information query function. Provides a convenient target query function, which can easily obtain the description information of the specified target in the scene.

Realistic visual simulation effect. Through the processing of remote sensing images, the establishment of digital elevation models, the import of road and river system vector layers, and the designation of regional detail maps, etc., the film-level scene browsing effect can be quickly produced. Supports special displays such as sky, clouds, sunlight and shadows, and fogging.

3. Three-dimensional analysis and display function module

Provide the interface of GIS analysis function and comprehensive three-dimensional analysis function. Terrain data can be queried, and terrain data can be statistically analyzed. It supports various ways of measuring the terrain in a visual way, and visualizes the measurement results in an intuitive way. It can perform terrain analysis, line-of-sight analysis, and flood analysis.

4. Visualization function module of digital simulation model

Taking the application of water resource protection as an example, it can provide the interface of water resource model, apply remote sensing, telemetry, GPS, microwave and other information to obtain real-time data provided by the system, and develop a perfect system with three-dimensional computer vision effect on the system platform to realize Visualization of rainstorm analysis model, rainfall runoff forecasting model, flood process calculation model, flood evolution model, etc., to realize data analysis of watershed natural geography, socio-economic and water conservancy engineering facilities, numerical simulation of flood evolution process and visual display of calculation results, etc. Quantitatively assess the risks of flood dispatching, flood detention areas, and embankment breaches under different flood conditions, and provide scientific basis for flood control decisions.

5. Visual function modules of engineering construction models and schemes

Site selection, planning, management and simulation control of engineering construction. The earthwork excavation volume and workload of engineering construction in different areas can be calculated as required. By creating a 3D scene, you can understand the overall picture of the project in advance, and use simulation technology to simulate the operation of various facilities.

Through triggers, sensors and other software plug-ins, various equipment can be simulated and controlled, the operation status can be displayed, and various complex changes of the facility can be simulated.

Virtual Reality (Virtual Reality-VR), also known as Lingjing, is a computer system that can create and experience a virtual world (Virtual World). The virtual world is the entire virtual environment (VirtualEnvironment) or the entirety of a given simulation object. The virtual environment is generated by the computer, and acts on the user through sight, hearing, touch, etc., so that it can produce an interactive visual simulation with an immersive feeling. Therefore, an immersive virtual environment system is composed of computer graphics Subsystems of considerable scale with different functions and levels, such as computer science, image processing and pattern recognition, intelligent interface technology, artificial intelligence technology, multi-sensor technology, voice processing and audio-visual technology, network technology, parallel processing technology, and high-performance computer systems Therefore, virtual reality technology is a highly comprehensive high-tech information technology, which has been widely used in military, medical, design, art, entertainment and many other fields.

Introducing virtual reality technology into each stage of system simulation can immerse people in it and have a clear understanding of the problems to be solved without having to observe the simulation results outside the screen, which will make model establishment and verification more convenient. Virtual reality technology is mainly reflected in: the computer uses artificial intelligence, pattern recognition and other technologies based on the established domain knowledge base and database, and the main control mechanism performs modeling, learning, planning and calculation. The visual simulation of this field is carried out through 3D animation production and display helmet; the simulation of this field is carried out through sensing mechanism and tactile gloves; the sound simulation is carried out through sound production and sound card; the dynamic simulation is carried out through mechanical control and transmission device. Then, the action responses of people to these sensory stimuli are fed back to the main control mechanism, thereby generating a simulation of a new sensory model in real time.

The structure and composition of the virtual reality subsystem are as follows: the hardware and software environment of the virtual reality subsystem of the digital earth prototype system is mainly composed of computer host system, control system, projection system, interactive equipment system and software system (scientific computing software, database management, 3D simulation software) consists of five parts.

The main features of this virtual reality subsystem are as follows:

1) High-performance graphics workstation

2) High-brightness projector and display screen (flat, spherical or cylindrical)

3) Edge blending and color correction electronic technology

4) Centralized data, audio and lighting control system (including touch-sensitive panel screen)

5) Stereo viewing device (sending device and active and passive stereo viewing glasses)

6) Input control devices (such as joysticks and trackballs)

7) Advanced output devices (such as headsets, and haptic devices)

8) High-fidelity, multi-channel audio/video playback system

9) Real-time 3D application software, toolkits and utilities

10) Large database management system and network technology

The technical problems of this virtual reality subsystem are as follows: using object-oriented (Object Oriented) technology, using high-end microcomputers and workstations as platforms, using simulation software MultiGen Creator, Vega, Performer, Motif, etc. as development tools, and building on SGI's IRIX operating system A virtual reality system operating in an environment.

1. Real-time simulation of geographical landscape in the region

This part mainly realizes real-time browsing of ultra-large-scale scenes, and provides a real-time simulation platform for rational utilization of resources, planning, and environmental protection. Specifically, it includes 1:100,000 and 1:10,000 terrain data using remote sensing image processing PCI software to generate a DEM library, and establishing 3D simulated terrain and landforms, including watershed topography, surface structure, etc., displaying hydrological stations, rain stations, hydrological survey points and City and other point information. Vector line attributes such as rivers, roads, and railways, surface information such as reservoirs and lakes, and block information such as buildings. Establish a geometric model library of related spatial objects (such as rivers, monitoring equipment, roads, water conservancy facilities, residential areas, crops) in the watershed, accurately locate each geometric object, and dynamically and real-time transfer the data of the model library during large-scale simulation Geometry model to browse.

2. Virtual visualization of the model

Provide an interface with thematic models, use the data provided by the models to conduct real-time simulations of weather systems, ecology, and environmental changes at various scales, providing advanced technical means for accurately revealing and grasping natural phenomena and their inherent laws. The development and evolution process of disasters (floods, droughts) can be viewed in real time, and it is really clear at a glance.

3. Assisted decision support based on virtual reality

Through the integration of the above parts, with the support of data and geographic models, and through various professional models, an auxiliary decision support system based on virtual reality is finally formed.

Establish a comprehensive knowledge base, which should cover relevant national laws, regulations, and various policies, experience and lessons in dealing with similar issues in history, watershed planning, planning in the watershed and related areas, layout of main and tributary projects, and Its specific requirements, restrictive factors of economic and social development, etc., thus forming an auxiliary decision-making support program library with strong visual performance capabilities, and comprehensively processing various related information.

The resource management system and its various projects often constitute a complex engineering system, and how to effectively manage these complex projects is a very difficult task. The conventional data management method adopts the combination of database and plane graphics. This method can only express the position attribute of the project, but cannot show the whole picture of the project. It is not vivid enough and lacks multimedia animation technology. Using advanced computer geographic information technology and Simulated reality technology, the spatial information is displayed intuitively in the form of a three-dimensional model to give people an immersive feeling. Space graphics as the background combined with multimedia means to display information, truly, intuitively and dynamically reproduce the natural landscape and the current project distribution and project construction conditions, obtain information that is difficult to obtain by conventional methods, and accurately locate and describe the project in a three-dimensional environment , so that decision-makers can have an overall and clear understanding, and help decision-makers to realize scientific decision-making in engineering construction and management.

Based on the above detailed description of the digital earth prototype system provided by the present invention, the basic theoretical framework of the digital earth and the interoperable extended model of the observation technology are proposed below. As shown in Fig. 12, Fig. 12 is a frame structure diagram of the digital earth prototype system provided by the present invention.

The research content of the digital earth prototype system CAS1.0 provided by the present invention is mainly divided into three aspects: basic theory research, key technology research and development and field application. At present, it has integrated nearly 3TB of data resources and dozens of database systems, and has provided services and services for crop yield estimation, disaster monitoring, urban change remote sensing dynamic monitoring, digital city, digital Olympics, energy, archaeology, tourism and other fields. support. The following is a detailed description again from the two aspects of software and hardware environment construction and system framework structure.

1. Construction of hardware and software environment:

1.1. EOS/MODIS receiving system

The EOS/MODIS receiving system consists of the following subsystems: antenna and control subsystem (antenna, feed, servo and control); channel subsystem (low noise amplifier, up-converter, down-converter, demodulator); data Intake subsystem (high-speed frame synchronization board); receiving and processing subsystem (computer and software package); massive data recording and storage subsystem; high-end data product subsystem.

Most of the hardware of the system has been localized, and at the same time, it has also absorbed some advanced foreign technologies, such as high-speed input board and receiving software. At present, the system is in good condition. Compared with other MODIS receiving systems, it has the following technical characteristics: novel antenna design, high tracking accuracy, and small wind moment; dual-channel functions, which can receive data from two satellites (TERRA and AQUI); The whole process of receiving and processing is automated; when the system is receiving, the real-time color image snapshot can be viewed; real-time system operation parameter report; calibration data processing and calculation speed is fast; automatic generation of retrieval and browsing image files; classification and automatic archiving of received and processed data; Intelligent management of archive resources; online automatic reading of orbit forecast parameters; generation of infrared band images (non-visible light); real-time display of sub-satellite point trajectories (graphical interface).

1.2 System Integration Hardware Environment

Host system: one SGI 3200 super graphics workstation

Control and interaction system: a set of orientation tracker and a set of Crestron centralized control system Projection system: 3 sets of Barco909 high-end projectors (including a set of 10-meter chord length and 3-meter-high curved cylindrical screen)

Other equipment: 50 pairs of stereoscopic glasses, one SGI 330NT workstation, several microcomputers, audio equipment, high-precision scanners, digital cameras, recorders, tape drives, 360G disk arrays, etc.

1.3 System development software environment

Programming language: VC++6.0, C in Unix environment, C++, Motif

Image processing and GIS software: PCI, Arcinfo 8.0, Arcview3.1 PhotoShop, etc.

Simulation modeling tools: MultiGen Creator, Maya, 3D Max, AutoCad

Scene driver: Vega, OpenGL, OpenGL Performer

2. Framework of Digital Earth Prototype System CAS1.0

2.1 The Digital Earth system consists of three basic functional layers: the basic data layer, the Digital Earth service layer and the user layer, as shown in Figure 13, which is a schematic diagram of the three basic functional layers of the Digital Earth prototype system provided by the present invention.

2.2 Basic functions of the digital earth prototype system CAS1.0:

Surface model analysis: It can generate plane/stereo contour maps, analyze topographical factors (such as slope, aspect, ridge coefficient, etc.), measure distances, make profile maps, and automatically extract water systems based on topographical data.

3D surface rendering: Provides a powerful 3D interactive terrain visualization environment for TIN models and grid models, supports multiple rendering methods such as elevation layered coloring, remote sensing image overlay, and vector data overlay; realize multi-angle real-time observation of 3D scenes, Dynamic flight simulation; provide output functions such as terrain rendering, flight scene recording, etc.

Spatial overlay analysis: Provide area-to-area, line-to-area, point-to-area, area-to-point, point-to-line overlay analysis, etc., supporting high-efficiency and large-scale data analysis.

Elevation library management: use pyramid structure to store raster data with multiple spatial resolutions, and realize fast and seamless roaming and extraction of the entire terrain data.

Model application: provide practical analysis functions such as elevation point labeling and mapping, connection visibility and viewing area, earthwork calculation, surface optimal path, watershed, and flood inundation.

2.3 Digital Earth Prototype System Data Framework

Digital Earth data structure, multi-resolution remote sensing data as the basic content data framework, realizes massive data storage, and manages the data structure of 3D landscape. As shown in FIG. 14, FIG. 14 is a schematic diagram of the frame structure of the digital earth prototype system provided by the present invention.

2.4 Architecture:

The digital earth prototype system CAS1.0 adopts a three-layer architecture, and the overall structure is shown in Figure 15, which is a schematic diagram of the system structure of the digital earth prototype system provided by the present invention.

A three-tier structure can be used to solve:

Various servers in the firm, the number or type of which exceeds 50;

the application is written in a different language;

Multiple or more heterogeneous data sources, such as different DBMS or file systems;

High workload, such as more than 50,000 transactions per day or more than 300 concurrent users accessing the same database on the same system;

Applied to internal and external communications of enterprises.

2.5 Composition of Digital Earth Prototype System CAS1.0:

After four years of construction, the digital earth prototype system CAS version 1.0 has completed the establishment of multiple subsystems such as data reception and fast processing, grid computing, spatial information database, metadata service, model library, map service and virtual reality. In the whole process, from data acquisition to data analysis and display expression, the subsystems are closely connected to form a digital earth working platform.

Combining the above introductions, it can be seen that the digital earth prototype system CAS1.0 integrates the most advanced technology today and successfully applies it to practical applications. It has successfully explored a set of theories and methods for researching and realizing digital earth, and through continuous upgrading and improvement, it fundamentally solves the embarrassing situation that digital earth has remained in the theoretical stage for a long time.

The specific embodiments described above have further described the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (16)

1. A digital earth prototyping system, comprising:

the data receiving and rapid processing subsystem is used for receiving and processing data transmitted by the satellite-borne medium-resolution imaging spectrometer MODIS, rapidly processing and warehousing the data, outputting the data to the metadata service subsystem, and providing a basic data source for testing and application of other subsystems in the digital earth prototype system;

the metadata service subsystem is used for managing metadata information in a database of the whole system, providing service for reasonably and quickly utilizing the metadata information in the database for the system, outputting the metadata information to the spatial information database subsystem and the model base subsystem, and acquiring a general model of an algorithm and an application program from the model base subsystem;

the model base subsystem is used for providing a general model of an algorithm and an application program for the spatial data processing and analysis of the digital earth prototype system, and respectively realizes the sharing and intercommunication of general model data with the metadata service subsystem, the spatial information database subsystem, the grid computing subsystem, the map application service subsystem and the virtual reality subsystem;

the spatial information database subsystem is used for managing spatial data in the digital earth prototype system, carrying out standardized processing, extraction and analysis on the spatial data objectively simulating and reflecting the real world to form a database suitable for multi-field large-scale application, and respectively realizing data sharing and intercommunication with the model database subsystem, the grid computing subsystem, the map application service subsystem and the virtual reality subsystem;

the grid computing subsystem is used for acquiring data from a remote data server or a local server according to user requirements, triggering service and monitoring the running state of the service, dividing a task into a plurality of subtasks, then distributing the subtasks to computers in the grid computing pool for execution, recovering and integrating execution results, returning the results to the user, and respectively realizing data sharing and intercommunication with the model base subsystem and the spatial information database subsystem;

the map application service subsystem is used for realizing the visualization of global information under the Internet environment and respectively realizing the sharing and intercommunication of data with the model base subsystem, the virtual reality subsystem and the spatial information database subsystem;

and the virtual reality subsystem is used for modeling, learning, planning and calculating by applying artificial intelligence and pattern recognition technology according to the established domain knowledge base and the database, realizing visual, tactile, auditory and dynamic virtual reality simulation, and respectively realizing data sharing and intercommunication with the model base subsystem, the map application service subsystem and the spatial information database subsystem.

2. The digital earth prototype system of claim 1, wherein the data receiving and fast processing subsystem comprises:

the ground station control system module is used for controlling and monitoring the operation condition of each part of the ground station and operating in a direct command mode or a preset program mode;

the satellite acquisition and tracking module is used for realizing satellite orbit report calculation, orbit parameter display and orbit selection;

the data receiving and quick viewing module is used for realizing data receiving and data quick viewing;

the data processing module is used for unpacking, regulating, positioning and calibrating and correcting the received and preprocessed original data to generate MODIS format data of 0-level, 1A-level and 1B-level standards;

the data production module is used for generating 0-level, 1A-level and 1B-level MODIS standard data products, wherein the 1B-level product format is a standard earth observation system/hierarchical data format EOS/HDF, and provides high-level data products for land, ocean and atmosphere remote sensing applications;

the data playback and editing module is used for performing playback display and editing processing on the generated data product;

the data distribution and backup module is used for intelligently archiving, distributing, managing and backing up all levels of data received and processed by the system;

and the application software module is used for providing MODIS data geometric accurate correction, MODIS data national mosaic and administrative boundary superposition, fire monitoring, flood monitoring, drought monitoring, surface temperature inversion, vegetation index calculation, cloud detection, sea water color and snow monitoring information.

3. The digital earth prototype system according to claim 2, wherein the ground station control system module provides a user menu of a graphical interface for implementing multi-orbit reception task setting, task schedule and execution status display for a plurality of satellites, real-time display of various status parameters during reception, event information recording file and browsing, subsystem status configuration operation and disk file management for a pre-programmed manner.

4. The digital earth prototype system according to claim 2, wherein the satellite acquisition and tracking module performs the calculation of the satellite orbit report by acquiring two lines of parameters when the satellite orbit report calculation is performed; when the orbit parameter display is realized, displaying all orbit parameters of the transit satellite in a text mode; when the orbit selection is realized, a user randomly selects the satellite orbit and the corresponding parameters which are to be received according to the requirement.

5. The digital earth prototype system according to claim 2,

when the DATA receiving and quick viewing module is used for receiving DATA, automatically receiving satellite signals according to a selected transit satellite orbit parameter and a satellite orbit time table, and preprocessing the received signals at least including amplification, frequency conversion, demodulation, output and storage to obtain original DATA RAW DATA;

and the data receiving and quick viewing module displays the transit satellite orbit, the orbit currently received and the progress thereof when realizing the quick viewing of the data, and displays the received data in a color image form in real time.

6. The digital earth prototype system of claim 2, wherein the data playback and editing module editing the generated data product comprises:

image synthesis, which is used for carrying out multi-channel synthesis display on data of any wave band to manufacture a color image;

image enhancement for performing enhancement processing on the display image;

the data projection transformation is used for performing projection transformation at least comprising Lagbotu, polar ray red plane, Mccatu, equal longitude and latitude and Gauss-Krigger on the image and displaying the transformed image in real time;

the layer superposition is used for superposing a geographic information layer at least comprising a longitude and latitude grid, an administrative district and a geographic mark on the image;

adding geographic information, adding common graphic symbols and annotation characters on the image, and editing the added graphic symbols and annotation characters;

image data extraction, which is used for segmenting the image, digging the image according to province and city boundaries or custom boundaries and extracting data;

the data format conversion is used for outputting the HDF data according to a bitmap image file BMP or a 2-system format;

and image splicing is used for splicing, synthesizing and displaying multi-track and multi-day data on the basis of the regional data set.

7. The digital earth prototype system according to claim 2, wherein the application software module is based on algorithms and standards published by the NASA, has image processing function, is oriented to remote sensing applications, and adopts a design framework of public high-level data products, discloses algorithms and discloses standards.

8. The digital earth prototype system of claim 1, wherein the metadata service subsystem comprises:

the spatial metadata gateway is used for realizing information interaction between the client and the server;

the spatial metadata server is used for receiving the information from the spatial metadata gateway, calling a corresponding functional module after analyzing the information, returning a result set if the result set needs to be returned, organizing the result, returning the result to the client in the form of an extensible markup language (XML) document, and issuing the spatial metadata on the Internet;

the space metadata base manager is used for acquiring, storing, managing and maintaining space metadata;

the spatial metadata query tool is used for providing an interface for the client to manage metadata mode information, metadata records and various mapping relations, including adding, deleting, modifying and browsing the metadata records;

the spatial metadata management tool consists of a user interface module and a protocol processing transmission module, wherein the user interface module is used for realizing interaction with a user, inputting query conditions and presenting query results; the protocol processing transmission module is used for organizing the query parameters collected by the user interface module into query statements, sending the query statements to the space metadata server through a TCP/IP protocol, then querying the space metadata database, and sending the query results to the user interface module for display.

9. The digital earth prototype system according to claim 1, wherein the generic models stored in the model library subsystem comprise at least:

the system comprises an improved homomorphic filtering cloud removing model, a pyramid structure remote sensing data rapid compression and playback model, a multi-scale remote sensing image wavelet fusion model, a hyperspectral remote sensing image cube model, a surface temperature inversion model, a spline multi-scale image representation and registration model, a radar data soil moisture inversion model, a vegetation index model biomass model, a net primary productivity model, a microwave remote sensing data ground parameter inversion model and a radar interferometry INSAR model.

10. The digital earth prototype system of claim 1, wherein the spatial information database subsystem comprises:

the spatial data processing module is used for realizing control of bottom layer data processing, bottom layer control frame layout and bottom layer data precision inspection;

the remote sensing data processing module is used for realizing the establishment of a homonymous control point library, measurable fine processing inspection and image metadata library;

the characteristic information processing module is used for realizing color reduction and enhancement, image mosaic color matching, non-homologous data assimilation and multi-scale data fusion;

and the thematic information analysis module is used for realizing various remote sensing application analyses based on a standard remote sensing data platform on the remote sensing data results.

11. The digital earth prototype system of claim 1, wherein the grid computing subsystem comprises:

the flow control module is used for controlling the flow by adopting a workflow technology and taking application as a unit;

the grid middleware is used for centrally managing and scheduling bottom hardware computing resources, distributing tasks submitted by an upper layer to available computers according to a certain strategy for computing, and recovering results;

the initialization module is used for preparing for special processing, and comprises data acquisition, log file creation, remote data acquisition, decompression, subtask segmentation and packaging, data format conversion and interface conversion;

the special processing module is used for providing earth surface temperature inversion, aerosol optical thickness inversion, normalized vegetation index, enhanced vegetation index, soil regulation vegetation index, land cover information classification, cloud detection, geometric correction, image shearing, image splicing and image matching for the nodes;

and the post-processing module is used for combining the calculation results of the subtasks to generate an HDF file, compressing the HDF file and putting the HDF file into a result database.

12. The digital earth prototype system of claim 11, wherein, in the topic processing module,

the surface temperature inversion comprises: by a mobile windowing self-adaptive split window method, inversion of the earth surface temperature is realized by using thermal infrared data of MODIS;

the aerosol optical thickness inversion comprises: extracting aerosol optical thickness parameters of MODIS images by using a dark pixel method, and realizing the inversion of the aerosol optical thickness;

the normalized vegetation index includes: firstly, carrying out geometric correction on MODIS data, and then generating NDVI through single data to realize normalized vegetation index;

the enhanced vegetation index comprises: firstly, carrying out geometric correction on MODIS data, and then generating an EVI through single data to realize an enhanced vegetation index;

the soil regulating vegetation index comprises: firstly, carrying out geometric correction on MODIS data, and then generating SAVI through single data to realize soil regulation vegetation index;

the land cover information classification includes: firstly, carrying out geometric correction on MODIS data, and then carrying out unsupervised classification by utilizing multiband information of a remote sensing image to realize land cover information classification;

the cloud detection comprises: firstly, carrying out geometric correction on MODIS data, and then filtering out clouds in an image to realize cloud detection;

the geometric correction includes: performing geometric correction on MODIS data with the resolution of 1KM by using longitude and latitude information with the resolution of 1KM in 1B data with the resolution of 500m of MODIS as a control point, wherein the closest interpolation method is adopted in a gray resampling method to realize the geometric correction;

the image cropping includes: firstly, carrying out geometric correction on MODIS data, and then cutting out a smaller research area from a large image to realize image cutting;

the image stitching comprises: firstly, carrying out geometric correction on MODIS data, and splicing a plurality of adjacent MODIS 1B data images into a larger complete image through geographic coordinate data inside the MODIS data to realize image splicing;

the image matching includes: firstly, carrying out geometric correction on MODIS data, and then obtaining the same region data through the positioning information of each pixel obtained by the result of the geometric correction, so as to generate a plurality of images matched with the same region and realize image matching.

13. The digital earth prototype system according to claim 1, wherein the map application service subsystem manages spatial data and attribute data through node-based attribute binding, hierarchical management technology and R-tree technology, and uses skin + skeleton technology to realize mass data storage and dynamic loading and display by means of distributed storage and distributed operation technology, and at least comprises a visualization module and a query module, and is an information window served by the prototype system platform.

14. The digital earth prototype system according to claim 13,

the visualization module is used for controlling a map through a menu command, carrying out stepless amplification, calling an image with higher resolution in a display area along with the hierarchical amplification, observing from different angles, browsing according to a specified route, realizing the detailed observation of a certain town or even a smaller area from the global in summary, and realizing the roaming in any area of the world through a keyboard, a mouse or a navigation ball;

the query module is used for realizing data query of basic spatial data, hierarchical comprehensive display of various grades of vector diagrams and basic topographic maps and bidirectional spatial query of graphic information and attribute information in the GIS system.

15. The digital earth prototype system of claim 1, wherein the virtual reality subsystem comprises:

the mass data management function module is used for managing the data acquired in real time and reserving and sorting historical data within a period of time;

the real-time roaming browsing and editing function module of the large geographic landscape is used for realizing the real-time roaming browsing and editing of the large geographic landscape;

the three-dimensional analysis and display functional module is used for providing an interface with a GIS analysis function, performing a comprehensive three-dimensional analysis function, inquiring topographic data, performing statistical analysis on the topographic data, supporting the measurement of the terrain in various modes in a visual mode, and visualizing the measurement result in a visual mode;

the visualization function module of the digital simulation model is used for realizing the visualization of the digital simulation model;

and the visualization function module of the engineering construction model and the scheme is used for realizing the visualization of the engineering construction model and the scheme.

16. The digital earth prototype system according to claim 15, wherein the real-time roaming browsing and editing function module of large geographic landscape supports multi-view browsing and full-screen browsing, history playback, screen capture, flight route editing, target information query, achieving real visual simulation effect.

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