CN114594497A - Satellite radiometric calibration method for collaborative multi-source data - Google Patents
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Abstract
The invention discloses a satellite radiometric calibration method for cooperative multi-source data, which comprises the steps of enhancing spectral resolution and reconstructing spectral characteristics aiming at different measurement data and hyperspectral image data; fusing the high-resolution remote sensing image, the surface feature spectrum library, the terrain data and the surface temperature multidimensional data to realize surface coverage and terrain data scene construction; acquiring aerosol data, analyzing, screening and extracting a text format from the data; the method comprises the steps of building a user data file interface, storing data into a corresponding database according to an index format, and searching meteorological hydrological data; and simulating the radiance of the sensor by using a radiation transmission model according to the spectral data, the surface coverage and terrain data, the aerosol data and the meteorological hydrological data, and performing linear fitting on the radiance and the image gray value to obtain a calibration coefficient. The invention integrates the multi-source data, realizes the rapid extraction of data such as surface feature spectrum, atmosphere, surface coverage, terrain, hydrology and the like and the batch processing of the radiation transmission model, and improves the experimental efficiency and precision of absolute radiation calibration.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a satellite radiometric calibration method for collaborative multi-source data.
Background
With the increasing development of remote sensing, more and more remote sensing products are continuously applied to national key research projects such as national defense, agriculture, forestry, land utilization and the like. Radiometric calibration is a key step for realizing remote sensing quantification, absolute radiometric calibration is to convert a grey value of a remote sensing image into radiance with practical significance or atmospheric layer top reflectivity, and the current absolute radiometric calibration mainly comprises the following steps: laboratory radiometric calibration, on-satellite calibration, field calibration, etc. The remote sensing satellite sensor needs to perform laboratory radiometric calibration before launching and lifting off to obtain a laboratory radiometric calibration coefficient, but the satellite sensor is attenuated to different degrees due to influences of factors such as the surrounding environment and device aging after the satellite lifts off, so that the monitoring of the radiometric performance of the remote sensing satellite sensor and the updating of the absolute radiometric calibration coefficient are necessary to solve the problems.
The existing radiation calibration method developed in China mainly adopts a reflectivity-based method, which measures atmospheric parameters and surface feature spectral parameters of a calibration field at the satellite transit time, and then simulates equivalent radiance at the entrance pupil of a sensor by using a radiation transmission model, thereby completing the conversion from an image gray value to the top radiance of an atmospheric layer. Although the calibration method can accurately obtain the calibration coefficient to a certain extent, the calibration method is usually limited by weather conditions, satellite observation angles and earth surface observation instruments, and a large amount of manpower and financial resources are needed to ensure smooth calibration experiments, which undoubtedly brings huge workload for the radiation performance monitoring of the remote sensing sensor. In addition, the radiation transmission models such as MODTRAN require more parameters, and often require screening and extracting of required data from numerous data sources, so that the experimental work efficiency is low.
Disclosure of Invention
In order to solve the technical problems, the invention solves the technical problems that the absolute radiometric calibration data is difficult to obtain at present, the formats and types of data from different data sources are not uniform, and the experiment efficiency is low due to the difficulty in data screening.
The purpose of the invention is realized by the following technical scheme:
a satellite radiometric calibration method for collaborative multi-source data comprises the following steps:
the satellite radiometric calibration method for collaborative multi-source data comprises the following steps:
A. performing spectral resolution enhancement and spectral feature reconstruction aiming at different measurement data and hyperspectral image data;
B. fusing the high-resolution remote sensing image, the surface feature spectrum library, the terrain data and the surface temperature multidimensional data to realize surface coverage and terrain data scene construction;
C. acquiring aerosol data, analyzing, screening and extracting a text format from the data;
D. the method comprises the steps of building a user data file interface, storing data into a corresponding database according to an index format, and searching meteorological hydrological data;
E. and simulating the radiance of the sensor by using a radiation transmission model according to the spectral data, the surface coverage and terrain data, the aerosol data and the meteorological hydrological data, and performing linear fitting on the radiance and the image gray value to obtain a calibration coefficient.
One or more embodiments of the present invention may have the following advantages over the prior art:
the invention can analyze, screen and search data from different sources, different formats and different types, can realize the rapid extraction of spectrum, atmosphere, earth surface coverage, terrain, hydrology and other data required by the absolute radiometric calibration of the remote sensing image and the batch processing of the radiometric transmission model, and can improve the experimental efficiency and the calibration precision of the absolute radiometric calibration.
Drawings
FIG. 1 is a schematic flow chart of a satellite radiometric calibration method for collaborative multi-source data;
FIG. 2 is a flow chart of a hyperspectral reflectance spectral feature reconstruction module process;
FIG. 3 is a flowchart of an iterative decorrelation spectral resolution enhancement function process;
FIG. 4 is a road map for ground scene modeling based on multivariate data;
FIG. 5 is a diagram of a system for processing ground aerosol observation data;
FIG. 6 is a flow chart of the process of integrating water vapor data of the ground aerosol observation data;
FIG. 7 is a flowchart of the process of integrating the basic meteorological hydrological data.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings.
As shown in fig. 1, a schematic flow chart of a satellite radiometric calibration method for collaborative multi-source data includes:
1) hyperspectral reflectance spectral feature reconstruction
The measurement process of the hyperspectral instruments has an average effect on spectral characteristics, data measured by different hyperspectral instruments are affected differently by spectral response of the instruments, the spectral characteristics are different, and the quantitative application capability of the data is affected. Therefore, spectral resolution enhancement and spectral feature reconstruction need to be performed on spectral curve data and hyperspectral image data obtained by different instruments, so that the spectral data of different instruments are unified. Firstly, data selection is carried out, a spectrum curve and a hyperspectral image data cube can be input, and the format supports text files and remote sensing data standard formats such as SLI, BSQ, BIP, BIL and the like. Spectral response parameters are then input, supporting three modes: appointing the central wavelength and the half-height width of each wave band; specifying a spectral response curve; and designating other standard remote sensing data header files. Thereafter, a selection is made whether to perform iterative decorrelation to enhance the data spectral resolution. Sequentially interpolating the hyperspectral data or the original data after the spectrum enhancement, sampling the spectral response to obtain spectral reconstruction data, and outputting the spectral reconstruction data. The method comprises the following steps of performing spectrum characteristic reconstruction on spectrum curve data in a text file or spectrum library format spectrum by spectrum; the hyperspectral image data cube is subjected to pixel-by-pixel spectral feature reconstruction, and the whole experimental flow is shown in fig. 2. The algorithm for enhancing the data spectral resolution by iterative decorrelation is mainly as follows:
(a) current hyperspectral data S1Taking as input hyperspectral data S; utilizing cubic spline interpolation to process current hyperspectral data S1Carrying out encryption sampling to obtain encrypted sampled hyperspectral data S2；
(b) According to the spectral response characteristic corresponding to the input hyperspectral data S, the encrypted and sampled hyperspectral data S2Response sampling is carried out to obtain response hyperspectral data S3；
(c) Judging response hyperspectral data S3And whether the input hyperspectral data S satisfies the following equation:
||S-S3||＜c
wherein ■ represents norm operation, c is a constant with a value of minimum; if not, turning to the step (d); if yes, turning to the step (e);
(d) according to the input high spectral data S and the response high spectral data S3Is designed to correct the value ScFor the current hyperspectral data S1And (5) correcting:
S1＝S1+Sc
turning to step (b);
(e) taking the spectral resolution enhanced hyperspectral data S' as the current hyperspectral data S1And S' is the final resolution enhancement hyperspectral data.
The experimental flow of the iterative decorrelation enhancement data spectral resolution is shown in fig. 3.
2) Earth surface coverage and terrain data scene construction
In the hyperspectral satellite data product processing, the comparison and the inspection with the ground condition are needed, so that the existing multivariate data are needed to be fused to form a ground scene. The method for comprehensively researching scene construction at home and abroad comprises three processes of surface feature spectrum processing, surface feature texture processing and hyperspectral scene generation. Firstly, necessary adjustment is carried out on spectral information and texture information to enable the spectral information and the texture information to have the performance required by follow-up atmospheric radiation transmission characteristic simulation and sensor imaging simulation, then statistical information of each wave band of texture data and statistical information of a ground object spectrum in a corresponding wave band are utilized to carry out spectral screening and filling of each pixel to form a pixel-spectrum lookup table, finally, each ground object is combined according to spatial distribution to generate a ground surface hyperspectral scene, a required ground scene file is output according to a certain data storage mode, and the whole ground scene construction process is completed. Under the condition that high-spatial-resolution hyperspectral image data are not available, the measured spectra of different ground objects in a scene and high-spatial-resolution textures and ground object distribution of the whole scene are collected, spectrum filling is carried out by utilizing the consistency of the statistical relationship between the spectrum of the ground object and the textures, and a high-spatial-resolution hyperspectral reflectivity image is generated. Firstly, determining the types and the distribution conditions of ground features contained in a scene according to a ground feature distribution diagram, secondly, mixing the texture of the ground feature texture information according to the distribution of the ground features to enable the area of the ground feature texture to cover the area of the ground feature distribution in an image, thirdly, counting the gray distribution of the ground feature texture, calculating the radiation specific gravity (Z-Score) of the image by utilizing the gray variance and the mean value, fourthly, expanding a spectrum library to enable the spectrum number of the same type of ground features to meet the requirement of expressing the gray change of the texture diagram, fifthly, counting the distribution characteristics of each type of ground feature spectrum in the corresponding spectrum section of the texture diagram, calculating the spectrum Z-Score, and finally, comparing the image Z-Score with the spectrum Z-Score to perform spectrum mapping to realize the construction of the ground surface reflectivity scene, wherein the experimental process is shown in figure 4.
3) Integration of ground aerosol observation data
The ground aerosol observation data uses data provided by AERONET (global aerosol automatic observation network), the AERONET data is distributed through internet by network tools and FTP, and data transmission is carried out by using ASCII text format, and the data are compressed in zip files. Data formats include lev10,. alm,. siz,. ssa, etc. AERONET covers the main global area, at present, more than 500 sites are shared in the whole world, and various types of data such as spectral aerosol optical thickness, water-reducing capacity and aerosol inversion products under different aerosol states in 1993 are provided. In order to facilitate the user to read and view the data provided by AERONET and extract the data required by a specific user through subsequent further screening, a ground aerosol observation data processing system can be constructed, as shown in fig. 5. The ground aerosol observation data access module decompresses the data files in batch and modifies the suffixes of the data files into csv format files or other formats which can be opened by excel and are convenient to read and view. The juliant day timing method provided by AERONET is converted to a standard timing method. And analyzing, screening and extracting the data according to the requirements of the user, and finally writing the output data into a data format file required by the user, wherein the experimental flow is shown in fig. 6.
4) Integration and processing of meteorological hydrological basic data
By constructing a user data file interface, reading various data according to naming structures and storage structure characteristics of different types of meteorological data files, storing the data into a corresponding database according to an index format, realizing meteorological data search, outputting meteorological data files and other data management tasks in a certain format. Based on naming and structural characteristics of different meteorological data, data are read in batch from different meteorological data files and stored in a database according to categories, and when the data are used, the data are retrieved in the database according to indexes. When outputting meteorological data, outputting corresponding data in the database according to the management command and format, and the experimental flow is shown in fig. 7.
5) Batch run and linear fitting of radiation transmission models
Acquiring the spectral data, the surface coverage and terrain data, the aerosol data and the meteorological hydrological data according to the information such as the observation time and the observation place of the sensor to be calibrated, and inputting the spectral data, the surface coverage and terrain data, the aerosol data and the meteorological hydrological data into radiation transmission models such as MODTRAN (modulated transit radar) to obtain the top radiance of the atmospheric layer of the sensor; and carrying out linear fitting on the image gray value of the sensor to be calibrated and the top radiance of the atmospheric layer of the sensor to obtain the absolute radiometric calibration coefficient of the sensor to be calibrated.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A satellite radiometric calibration method in cooperation with multi-source data, comprising:
A. performing spectral resolution enhancement and spectral feature reconstruction aiming at different measurement data and hyperspectral image data;
B. fusing the high-resolution remote sensing image, the surface feature spectrum library, the terrain data and the surface temperature multidimensional data to realize surface coverage and terrain data scene construction;
C. acquiring aerosol data, analyzing, screening and extracting a text format from the data;
D. the method comprises the steps of building a user data file interface, storing data into a corresponding database according to an index format, and searching meteorological hydrological data;
E. and simulating the radiance of the sensor by using a radiation transmission model according to the spectral data, the surface coverage and terrain data, the aerosol data and the meteorological hydrological data, and performing linear fitting on the radiance and the image gray value to obtain a calibration coefficient.
2. The method for satellite radiometric calibration of cooperative crowd-sourced data as recited in claim 1, wherein said a comprises:
a1, selecting data, and inputting a spectral curve and a hyperspectral image data cube;
a2, inputting corresponding parameters of the spectrum;
a3, selecting whether to carry out iteration decorrelation to enhance the data spectrum resolution; if so, performing iterative decorrelation to enhance the spectral resolution of the data, otherwise, performing interpolation on the spectral data;
a4, sequentially interpolating the spectrally enhanced hyperspectral data or original data, sampling spectral response to obtain spectral reconstruction data, and outputting the spectral reconstruction data; the spectral characteristic reconstruction method comprises the steps of carrying out spectral characteristic reconstruction on spectral curve data in a text file or spectrum library format spectrum by spectrum, and carrying out pixel-by-pixel spectral characteristic reconstruction on a hyperspectral image data cube.
3. The method for satellite radiometric calibration of cooperative crowd-sourced data as recited in claim 2, wherein said algorithm for iteratively decorrelating and enhancing spectral resolution of data in a3 comprises:
(a) current hyperspectral data S1Taking as input hyperspectral data S; utilizing cubic spline interpolation to process current hyperspectral data S1Carrying out encryption sampling to obtain encrypted sampled hyperspectral data S2；
(b) According to the spectral response characteristic corresponding to the input hyperspectral data S, the encrypted and sampled hyperspectral data S2Response sampling is carried out to obtain response hyperspectral data S3；
(c) Judging response hyperspectral data S3And whether the input hyperspectral data S satisfies the following formula:
||S-S3||＜c
wherein ■ represents norm operation, c is a constant with one value being minimum; if not, turning to the step (d); if yes, turning to the step (e);
(d) according to the input high spectral data S and the response high spectral data S3Is designed to correct the value ScFor the current hyperspectral data S1And (3) correcting:
S1＝S1+Sc
turning to step (b);
(e) taking the spectral resolution enhanced hyperspectral data S' as the current hyperspectral data S1。
4. The method for satellite radiometric calibration of cooperative crowd-sourced data as recited in claim 1, wherein said B comprises:
b1 adjusting the spectrum information and the texture information to meet the performance required by the subsequent atmospheric radiation transmission characteristic simulation and the sensor imaging simulation;
b2, carrying out spectrum screening and filling on each pixel by utilizing the statistical information of each wave band of the texture data and the statistical information of the surface feature spectrum in the corresponding wave band to form a pixel-spectrum lookup table;
b3, combining all ground objects according to spatial distribution to generate a ground surface hyperspectral scene, and outputting a required ground scene file according to a certain data storage mode to complete the whole ground scene construction process;
b4 if the high spatial resolution hyperspectral image is not available, filling the spectrum by using the consistency of the statistical relationship between the surface feature spectrum and the texture to generate the high spatial resolution hyperspectral reflectivity image.
5. The method for satellite radiometric calibration of cooperative crowd-sourced data as recited in claim 4, wherein said B4 comprises:
1) confirming the types and the distribution conditions of the ground objects contained in the scene according to the ground object distribution diagram;
2) mixing the texture of the ground feature texture information according to the distribution of the ground features, so that the area of the texture of the ground features covers the area of the distribution of the ground features in the image;
3) counting the gray distribution of the ground feature texture, and calculating the image radiation proportion by using the gray variance and the mean value;
4) expanding the spectrum library to enable the spectrum quantity of the same type of ground objects to meet the requirement of expressing the gray level change of the texture map;
5) and (4) counting the distribution characteristics of the spectra of each type of ground objects in the corresponding spectrum section of the texture map, calculating the spectrum Z-Score, and finally comparing the image Z-Score with the spectrum Z-Score to perform spectrum mapping so as to realize the construction of the earth surface reflectivity scene.
6. The method for satellite radiometric calibration of cooperative crowd-sourced data as recited in claim 1, wherein said C comprises:
c1, reading the naming structure characteristics of different aerosol data in the AERONET in batches, analyzing the file name structure, searching key sign words, and classifying different types of data files;
c2, realizing AERONET data format conversion function, julian day conversion function, characteristic data extraction function, output data format conversion and multiple output data file merging function;
and C3, screening and extracting the data according to the date, the data type, the data content and the file format parameters according to the requirements.
7. The method for satellite radiometric calibration of cooperative crowd-sourced data as recited in claim 1, wherein said D comprises:
d1, reading in data in batches from different meteorological data files based on different meteorological data naming and structural characteristics, and storing the data in a database according to types;
d2, searching in the database according to the index, and outputting the corresponding data in the database according to the format according to the management command when outputting the meteorological data.
8. The method for satellite radiometric calibration of cooperative crowd-sourced data as recited in claim 1, wherein said E comprises:
e1, inputting the spectral data, the ground surface coverage and terrain data, the aerosol data and the meteorological hydrological data which are acquired according to the observation time and the observation place information of the sensor to be calibrated into a MODTRAN radiation transmission model to obtain the top radiance of the sensor atmosphere layer;
and E2, performing linear fitting on the image gray value of the sensor to be calibrated and the top radiance of the sensor atmosphere layer to obtain the absolute radiometric calibration coefficient of the sensor to be calibrated.
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