CN114113000A - Method, system and equipment for improving spectrum reflectivity inversion based on empirical linear model - Google Patents
Method, system and equipment for improving spectrum reflectivity inversion based on empirical linear model Download PDFInfo
- Publication number
- CN114113000A CN114113000A CN202111526701.5A CN202111526701A CN114113000A CN 114113000 A CN114113000 A CN 114113000A CN 202111526701 A CN202111526701 A CN 202111526701A CN 114113000 A CN114113000 A CN 114113000A
- Authority
- CN
- China
- Prior art keywords
- data
- ground
- aviation
- spectral
- hyperspectral
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000002310 reflectometry Methods 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000001228 spectrum Methods 0.000 title claims description 40
- 230000003595 spectral effect Effects 0.000 claims abstract description 136
- 230000001360 synchronised effect Effects 0.000 claims abstract description 57
- 238000007781 pre-processing Methods 0.000 claims abstract description 15
- 238000012216 screening Methods 0.000 claims abstract description 10
- 238000012937 correction Methods 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 230000006870 function Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000013480 data collection Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 235000012054 meals Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Image Processing (AREA)
- Spectrometry And Color Measurement (AREA)
Abstract
The invention particularly relates to an improved spectral reflectivity inversion method, system and device based on an empirical linear model, wherein the improved spectral reflectivity inversion method based on the empirical linear model comprises the following steps: acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data; preprocessing the aviation hyperspectral data; matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position, and establishing a data set; establishing a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and generating aviation high spectral reflectivity image data according to the linear model, so that the problems in high spectral reflectivity inversion of the traditional empirical linear method are solved, and the reflectivity inversion accuracy of the empirical linear method is improved.
Description
Technical Field
The invention belongs to the technical field of remote sensing, and particularly relates to an improved spectrum reflectivity inversion method, system and equipment based on an empirical linear model.
Background
In remote sensing, the reflectivity of a ground object can reflect the spectral characteristics of the ground object more stably, the characteristics of the ground object are determined by the molecular type and the structure of a substance, and the ground object has a fingerprint effect, namely, a specific ground object type corresponds to a specific reflectivity spectrum. Compared with the radiant brightness, the reflectivity can reflect the essential characteristics of the ground features. In addition, due to the influence of the atmosphere, certain errors exist between the measured spectral characteristics and the true spectral characteristics of the ground objects. Therefore, accurate reflectivity is the key to ensure the accuracy of information extraction and quantitative research in the later data processing process.
The empirical linear method is a traditional reflectivity inversion method, however, the method has the following problems in the reflectivity conversion process that (1) the reflectivity conversion is carried out integrally, the real-time change of the solar illumination and the atmospheric environment in the data acquisition process of a remote sensor is not considered, and the conversion is carried out according to the acquisition time; (2) the ground object data of the synchronous points are not accurate enough, the arrangement is unreasonable, the synchronism is poor, and the like. The above problem causes a large error when the method performs reflectivity inversion.
Therefore, there is a need to design a new method, system and apparatus for improved spectral reflectance inversion based on empirical linear models based on the above technical problems.
Disclosure of Invention
The invention aims to provide a method, a system and equipment for improving spectral reflectivity inversion based on an empirical linear model.
In order to solve the above technical problem, the present invention provides a method for inverting a spectral reflectance, comprising:
acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data;
preprocessing the aviation hyperspectral data;
matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position, and establishing a data set;
establishing a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and
and generating aviation high spectral reflectivity image data according to the linear model.
Further, the method for acquiring aviation hyperspectral data, synchronous airline ground spectral data and ground real-time observation spectral data comprises the following steps:
determining a collecting route of synchronous or quasi-synchronous route ground spectrum measurement according to the flight route condition and the ground feature condition, determining the types of the ground features on the collecting route, the spectrum number collected by various types of ground features and the range of each collecting point;
in the working area, the white board is subjected to real-time spectrum acquisition through a ground object spectrometer, the white board is recorded at the frequency of one spectrum per second, and data acquisition and aviation hyperspectral data acquisition are carried out synchronously.
Furthermore, the types of the ground objects on the collection route are not less than three;
the number of the spectra collected by the same ground object is not less than five.
Further, the method for preprocessing the aviation hyperspectral data comprises the following steps:
and (4) carrying out radiance conversion and geometric correction on the aviation hyperspectral data.
Further, the method for matching the ground real-time observation spectrum data with the aviation hyperspectral radiance value data comprises the following steps:
and matching ground real-time observation spectral data in the same time period by taking the aviation hyperspectral data acquisition time as a reference.
Further, the method for establishing the linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data comprises the following steps:
acquiring aviation hyperspectral initial reflectivity data according to the matched ground real-time observation spectral data and aviation hyperspectral data at the same time:
wherein R isaiPerforming high-spectrum initial reflectivity of each wave band for aviation; DNaiThe radiance value of each wave band of the aviation hyperspectral is obtained; DNbiReal-time observing the radiance value of each wave band of the spectrum for the ground;
according to the synchronous route ground spectral reflectance data and the aviation hyperspectral initial reflectance data of the same ground object, the aviation hyperspectral reflectance data are corrected to obtain correction coefficients k of all wave bands of all pixelsiAnd bi：
Rgi＝ki*Rai+bi；
Wherein R isgiThe reflectivity of each wave band of the ground synchronous point spectrum; raiPerforming high-spectrum initial reflectivity of each wave band for aviation; ki、biCorrecting coefficients for various wave bands of the aviation hyperspectral image;
constructing an aviation hyperspectral reflectivity inversion empirical linear model according to the correction coefficient:
Ri＝ki*Rai+bi；
wherein R isiAnd (4) correcting the reflectivity of each wave band for aviation hyperspectrum.
In a second aspect, the present invention further provides a system for inverting spectral reflectance, comprising:
the data acquisition module is used for acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data;
the preprocessing module is used for preprocessing the aviation hyperspectral data;
the matching module is used for matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening out synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position and establishing a data set;
the model building module is used for building a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and the image acquisition module is used for generating aviation high spectral reflectivity image data according to the linear model.
In a third aspect, the present invention further provides a spectral reflectance inversion apparatus, including:
the system comprises an aviation hyperspectral measurement system, a ground object spectrometer and a terminal;
the aviation hyperspectral measurement system is suitable for acquiring aviation hyperspectral data;
the surface feature spectrometer is suitable for acquiring real-time ground observation spectral data and synchronous air route ground spectral data;
the terminal is suitable for acquiring aviation hyperspectral reflectivity image data according to the aviation hyperspectral data, the synchronous air route ground spectral data and the ground real-time observation spectral data.
The method has the advantages that the aviation hyperspectral data, the synchronous air route ground spectral data and the ground real-time observation spectral data are acquired; preprocessing the aviation hyperspectral data; matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position, and establishing a data set; establishing a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and generating aviation high spectral reflectivity image data according to the linear model, so that the problems in high spectral reflectivity inversion of the traditional empirical linear method are solved, and the reflectivity inversion accuracy of the empirical linear method is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a spectral reflectance inversion method of the present invention;
fig. 2 is a specific flowchart of the spectral reflectance inversion method of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, this embodiment 1 provides a method for inverting spectral reflectance, including: acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data; preprocessing the aviation hyperspectral data; matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position, and establishing a data set; establishing a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and generating aviation high spectral reflectivity image data according to the linear model, so that the problems in high spectral reflectivity inversion of the traditional empirical linear method are solved, and the reflectivity inversion accuracy of the empirical linear method is improved.
As shown in fig. 2, in this embodiment, the method for acquiring aviation hyperspectral data, synchronous airline ground spectral data, and ground real-time observation spectral data includes: aviation hyperspectral data can be collected through a set of aviation hyperspectral measurement system, and synchronous air route ground spectral data and ground real-time observation spectral data are obtained through two ground object spectrometers; the method for acquiring the ground spectrum of the synchronous route comprises the following steps: determining a collecting route of synchronous or quasi-synchronous course ground spectrum measurement according to flight route conditions and ground feature conditions, determining the types of features on the collecting route, the number of spectrums collected by various types of features and the range of each collecting point, for example, ensuring synchronous spectrum collection of more than three types of features in the range of each course, collecting at least five spectrums for each type of feature, and ensuring that the pure feature area of each collecting point is not less than 3 multiplied by 3 meters; the method for collecting the ground real-time observation spectrum comprises the following steps: in the working area, the white board is subjected to real-time spectrum collection through a ground object spectrometer, the white board is recorded at the frequency of one spectrum per second, data collection and aviation hyperspectral data collection are synchronously carried out, the consistency of the external environment during aviation flight is ensured, and the collected spectrum value is used as a standard radiation area light source. Before data acquisition, the consistency check is needed to be carried out on the aviation hyperspectral measurement system and the ground object spectrometer, so that the ground object data of the synchronous point is accurate.
In this embodiment, the method for preprocessing the aviation hyperspectral data includes: and (4) carrying out radiance conversion and geometric correction on the aviation hyperspectral data.
In this embodiment, the method for matching the ground real-time observation spectrum data with the aviation hyperspectral radiance value data includes: and matching ground real-time observation spectral data in the same time period by taking the aviation hyperspectral data acquisition time as a reference.
In this embodiment, the method for establishing a linear model between an aviation hyperspectral radiance value and a reflectivity according to the matched ground real-time observation spectral data and the screened synchronous airline ground spectral data comprises the following steps: acquiring aviation hyperspectral initial reflectivity data according to the matched ground real-time observation spectral data and aviation hyperspectral data at the same time:
wherein R isaiPerforming high-spectrum initial reflectivity of each wave band for aviation; DNaiThe radiance value of each wave band of the aviation hyperspectral is obtained; DNbiReal-time observing the radiance value of each wave band of the spectrum for the ground;
correcting the aviation hyperspectral reflectivity data according to the synchronous route ground spectral reflectivity data and the aviation hyperspectral initial reflectivity data of the same point and the same ground object, and calculating correction coefficients k of all wave bands of all pixels by using least square fittingiAnd bi：
Rgi＝ki*Rai+bi；
Wherein R isgiThe reflectivity of each wave band of the ground synchronous point spectrum; raiPerforming high-spectrum initial reflectivity of each wave band for aviation; ki、biCorrecting coefficients for various wave bands of the aviation hyperspectral image;
constructing an aviation hyperspectral reflectivity inversion empirical linear model according to the correction coefficient:
Ri＝ki*Rai+bi；
wherein R isiAnd (4) correcting the reflectivity of each wave band for aviation hyperspectrum.
In the embodiment, aviation hyperspectral data in the Gaoyou lake region are selected to be subjected to spectrum reflectivity inversion based on improved empirical linear models, and other regions can refer to the method. ObtainingThe hyperspectral data of 137 strips with 10 frames has the area of about 900Km2The spectral wavelength interval is 400-2500nm, total 543 effective wave bands, the spatial resolution is 1 meter, and the visible light and short wave infrared spectral resolutions are 2.47nm and 7nm respectively; spectral data of ground objects of 483 synchronous or quasi-synchronous waypoints are acquired, spectral data of at least 3 kinds of ground objects are acquired for each strip, the spectral data of each ground object is measured for 5 times, GPS data of each point is recorded, the ground objects of each acquisition point are pure, and the area of each acquisition point is larger than 3 multiplied by 3 meters. The ground real-time observation spectral data with the consistent acquisition time of the aviation hyperspectral data of 10 shelves is obtained. Before data acquisition, consistency experiments of an aviation hyperspectral measurement system and 2 ground object spectrometers are carried out. And performing radiance conversion on the hyperspectral data of the Gaoyou lake to generate radiance data. The method comprises the steps of matching real-time ground observation spectrum data within the same time through the aviation hyperspectral data acquisition time, screening out aviation hyperspectral data and synchronous airline ground spectrum data of the same ground object at the same position according to aviation and ground GPS data, totaling 483 pieces, and establishing a data set. And establishing a linear model between the aerial hyperspectral radiance value and the reflectivity by combining the ground real-time observation spectral data and the screened synchronous air route ground spectral data. And according to the matched radiance value data and aviation hyperspectral radiance value data of the ground real-time observation white board at the same time, 543 aviation hyperspectral initial meal rate data of wave bands in each pixel of the aviation hyperspectral data are solved. Using the synchronous flight path ground object spectral reflectivity data and the aviation hyperspectral initial reflectivity data obtained by 4.1 to correct the aviation hyperspectral reflectivity data, and calculating correction coefficients k of all wave bands by using least square fittingiAnd bi. Taking the 50 th band of a certain pixel as an example, the obtained correction coefficient is 0.23, 1.3. According to the correction coefficient Ki，biAnd establishing an aviation hyperspectral reflectivity inversion empirical linear model. Taking a certain pixel 50 wave band as an example, the reflectance inversion empirical linear model of the pixel 50 wave band is that R is 0.23 × Ra50+1.3. And (4) solving the aviation hyperspectral reflectivity of the Gaoyou lake region according to the established reflectivity inversion empirical linear model.
Example 2
On the basis of embodiment 1, this embodiment 2 further provides a spectral reflectance inversion system, including: the data acquisition module is used for acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data; the preprocessing module is used for preprocessing the aviation hyperspectral data; the matching module is used for matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening out synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position and establishing a data set; the model building module is used for building a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and the image acquisition module is used for generating aviation high spectral reflectivity image data according to the linear model.
In this embodiment, specific functions of each module have been described in detail in embodiment 1, and are not described in detail in this embodiment.
Example 3
On the basis of embodiment 1, this embodiment 3 further provides a spectral reflectance inversion apparatus, including: the system comprises an aviation hyperspectral measurement system, a ground object spectrometer and a terminal; the aviation hyperspectral measurement system is suitable for acquiring aviation hyperspectral data; the surface feature spectrometer is suitable for acquiring real-time ground observation spectral data and synchronous air route ground spectral data; the terminal is suitable for acquiring aviation hyperspectral reflectivity image data according to the aviation hyperspectral data, the synchronous air route ground spectral data and the ground real-time observation spectral data.
In this embodiment, the terminal may acquire aviation hyperspectral reflectivity image data by using the spectral reflectivity inversion method in embodiment 1.
In conclusion, the invention acquires the aviation hyperspectral data, the synchronous airline ground spectral data and the ground real-time observation spectral data; preprocessing the aviation hyperspectral data; matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position, and establishing a data set; establishing a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and generating aviation high spectral reflectivity image data according to the linear model, so that the problems in high spectral reflectivity inversion of the traditional empirical linear method are solved, and the reflectivity inversion accuracy of the empirical linear method is improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (8)
1. A method of inverting spectral reflectance, comprising:
acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data;
preprocessing the aviation hyperspectral data;
matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position, and establishing a data set;
establishing a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and generating aviation high spectral reflectivity image data according to the linear model.
2. The method of inverting spectral reflectance according to claim 1,
the method for acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data comprises the following steps:
determining a collecting route of synchronous or quasi-synchronous route ground spectrum measurement according to the flight route condition and the ground feature condition, determining the types of the ground features on the collecting route, the spectrum number collected by various types of ground features and the range of each collecting point;
in the working area, the white board is subjected to real-time spectrum acquisition through a ground object spectrometer, the white board is recorded at the frequency of one spectrum per second, and data acquisition and aviation hyperspectral data acquisition are carried out synchronously.
3. The method of spectral reflectance inversion according to claim 2,
the types of the ground objects on the collection route are not less than three;
the number of the spectra collected by the same ground object is not less than five.
4. The method of spectral reflectance inversion according to claim 2,
the method for preprocessing the aviation hyperspectral data comprises the following steps:
and (4) carrying out radiance conversion and geometric correction on the aviation hyperspectral data.
5. The method of inverting spectral reflectance according to claim 4,
the method for matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data comprises the following steps:
and matching ground real-time observation spectral data in the same time period by taking the aviation hyperspectral data acquisition time as a reference.
6. The method of spectral reflectance inversion according to claim 5,
the method for establishing the linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data comprises the following steps:
acquiring aviation hyperspectral initial reflectivity data according to the matched ground real-time observation spectral data and aviation hyperspectral data at the same time:
wherein R isaiPerforming high-spectrum initial reflectivity of each wave band for aviation; DNaiThe radiance value of each wave band of the aviation hyperspectral is obtained; DNbiReal-time observing the radiance value of each wave band of the spectrum for the ground;
according to the synchronous route ground spectral reflectance data and the aviation hyperspectral initial reflectance data of the same ground object, the aviation hyperspectral reflectance data are corrected to obtain correction coefficients k of all wave bands of all pixelsiAnd bi：
Rgi＝ki*Rai+bi；
Wherein R isgiThe reflectivity of each wave band of the ground synchronous point spectrum; raiPerforming high-spectrum initial reflectivity of each wave band for aviation; ki、biCorrecting coefficients for various wave bands of the aviation hyperspectral image;
constructing an aviation hyperspectral reflectivity inversion empirical linear model according to the correction coefficient:
Ri＝ki*Rai+bi；
wherein R isiAnd (4) correcting the reflectivity of each wave band for aviation hyperspectrum.
7. A spectral reflectance inversion system, comprising:
the data acquisition module is used for acquiring aviation hyperspectral data, synchronous air route ground spectral data and ground real-time observation spectral data;
the preprocessing module is used for preprocessing the aviation hyperspectral data;
the matching module is used for matching the ground real-time observation spectral data with the aviation hyperspectral radiance value data, screening out synchronous air route ground spectral data and aviation hyperspectral data of ground objects at the same position and establishing a data set;
the model building module is used for building a linear model between the aviation hyperspectral radiance value and the reflectivity according to the matched ground real-time observation spectral data and the screened synchronous air route ground spectral data; and the image acquisition module is used for generating aviation high spectral reflectivity image data according to the linear model.
8. A spectral reflectance inversion apparatus, comprising:
the system comprises an aviation hyperspectral measurement system, a ground object spectrometer and a terminal;
the aviation hyperspectral measurement system is suitable for acquiring aviation hyperspectral data;
the surface feature spectrometer is suitable for acquiring real-time ground observation spectral data and synchronous air route ground spectral data;
the terminal is suitable for acquiring aviation hyperspectral reflectivity image data according to the aviation hyperspectral data, the synchronous air route ground spectral data and the ground real-time observation spectral data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111526701.5A CN114113000B (en) | 2021-12-14 | 2021-12-14 | Inversion method, system and equipment for improving spectral reflectivity based on empirical linear model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111526701.5A CN114113000B (en) | 2021-12-14 | 2021-12-14 | Inversion method, system and equipment for improving spectral reflectivity based on empirical linear model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114113000A true CN114113000A (en) | 2022-03-01 |
| CN114113000B CN114113000B (en) | 2024-08-16 |
Family
ID=80364442
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111526701.5A Active CN114113000B (en) | 2021-12-14 | 2021-12-14 | Inversion method, system and equipment for improving spectral reflectivity based on empirical linear model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114113000B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1556381A (en) * | 2003-12-30 | 2004-12-22 | 中国科学院上海技术物理研究所 | Aviation high spectrum remote sensing flight ground synchronous scaling and reflectivity conversion method |
| CN103543744A (en) * | 2013-09-10 | 2014-01-29 | 江苏省地质勘查技术院 | Method for arranging aeromagnetic survey flight paths of unmanned airship |
| CN108898070A (en) * | 2018-06-07 | 2018-11-27 | 湖南中科星图信息技术有限公司 | A kind of high-spectrum remote-sensing extraction Mikania micrantha device and method based on unmanned aerial vehicle platform |
| CN110514597A (en) * | 2019-09-04 | 2019-11-29 | 北京麦飞科技有限公司 | The diseases and pests of agronomic crop monitoring method of based on star remotely-sensed data collaboration |
| CN110887792A (en) * | 2019-12-17 | 2020-03-17 | 深圳慧格科技服务咨询有限公司 | River water quality rapid monitoring system based on unmanned aerial vehicle hyperspectral image |
| CN111670668A (en) * | 2020-06-05 | 2020-09-18 | 沈阳农业大学 | Accurate topdressing method for agricultural rice unmanned aerial vehicle based on hyperspectral remote sensing prescription chart |
| KR20210078108A (en) * | 2019-12-18 | 2021-06-28 | 동아대학교 산학협력단 | Monitoring method of ecological disturbance species using aerial hyperspectral imaging |
-
2021
- 2021-12-14 CN CN202111526701.5A patent/CN114113000B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1556381A (en) * | 2003-12-30 | 2004-12-22 | 中国科学院上海技术物理研究所 | Aviation high spectrum remote sensing flight ground synchronous scaling and reflectivity conversion method |
| CN103543744A (en) * | 2013-09-10 | 2014-01-29 | 江苏省地质勘查技术院 | Method for arranging aeromagnetic survey flight paths of unmanned airship |
| CN108898070A (en) * | 2018-06-07 | 2018-11-27 | 湖南中科星图信息技术有限公司 | A kind of high-spectrum remote-sensing extraction Mikania micrantha device and method based on unmanned aerial vehicle platform |
| CN110514597A (en) * | 2019-09-04 | 2019-11-29 | 北京麦飞科技有限公司 | The diseases and pests of agronomic crop monitoring method of based on star remotely-sensed data collaboration |
| CN110887792A (en) * | 2019-12-17 | 2020-03-17 | 深圳慧格科技服务咨询有限公司 | River water quality rapid monitoring system based on unmanned aerial vehicle hyperspectral image |
| KR20210078108A (en) * | 2019-12-18 | 2021-06-28 | 동아대학교 산학협력단 | Monitoring method of ecological disturbance species using aerial hyperspectral imaging |
| CN111670668A (en) * | 2020-06-05 | 2020-09-18 | 沈阳农业大学 | Accurate topdressing method for agricultural rice unmanned aerial vehicle based on hyperspectral remote sensing prescription chart |
Non-Patent Citations (4)
| Title |
|---|
| 伊丕源;李瀚波;童鹏;赵英俊;张川;田丰;车永飞;吴文欢;: "加入高程因子的航空高光谱影像大气辐射校正", 国土资源遥感, no. 02, 24 May 2019 (2019-05-24), pages 69 - 75 * |
| 李波: "机载高光谱数据辐射校正与融合方法", 《中国优秀硕士学位论文全文数据库》, 15 June 2020 (2020-06-15), pages 10 - 20 * |
| 陆冬华;赵英俊;张东辉;裴承凯;孙雨;秦凯;: "地面与航空高光谱反射率联合反演技术研究", 光谱学与光谱分析, no. 1, 15 October 2018 (2018-10-15), pages 333 - 334 * |
| 陈伟: "高光谱影像地物智能化提取理论与方法研究", 《中国博士学位论文全文数据库》, 15 June 2013 (2013-06-15), pages 5 - 15 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114113000B (en) | 2024-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Muller-Wilm et al. | Sentinel-2 level 2A prototype processor: Architecture, algorithms and first results | |
| Amorós-López et al. | Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring | |
| Börner et al. | SENSOR: a tool for the simulation of hyperspectral remote sensing systems | |
| Ji et al. | An agreement coefficient for image comparison | |
| CN112051222A (en) | River and lake water quality monitoring method based on high-resolution satellite image | |
| Minařík et al. | Radiometric and atmospheric corrections of multispectral μMCA camera for UAV spectroscopy | |
| US10373339B2 (en) | Hyperspectral scene analysis via structure from motion | |
| JPS63118889A (en) | Change detection system by picture | |
| Pagnutti et al. | Radiometric characterization of IKONOS multispectral imagery | |
| AU2017332863B2 (en) | Generating radiometrically corrected surface images | |
| EP2310984A2 (en) | Processing of remotely acquired imagery | |
| CN106940887B (en) | GF-4 satellite sequence image cloud and cloud shadow detection method | |
| Kabir et al. | Vicarious methodologies to assess and improve the quality of the optical remote sensing images: A critical review | |
| Sterckx et al. | Atmospheric correction of APEX hyperspectral data | |
| Schläpfer et al. | Cast shadow detection to quantify the aerosol optical thickness for atmospheric correction of high spatial resolution optical imagery | |
| CN103617629A (en) | High-resolution remote sensing image vegetation index time sequence correcting method based on moderate resolution imaging spectroradiometer (MODIS) remote sensing image | |
| US10650498B2 (en) | System, method, and non-transitory, computer-readable medium containing instructions for image processing | |
| Ariza et al. | Empirical line model for the atmospheric correction of sentinel-2A MSI images in the Caribbean Islands | |
| CN115343226A (en) | Multi-scale vegetation coverage remote sensing calculation method based on unmanned aerial vehicle | |
| CN113052153B (en) | Method and device for detecting remote sensing reflectivity image, electronic equipment and storage medium | |
| Brook et al. | Supervised vicarious calibration (SVC) of multi-source hyperspectral remote-sensing data | |
| Markelin | Radiometric calibration, validation and correction of multispectral photogrammetric imagery | |
| Harris et al. | Radiometric homogenisation of aerial images by calibrating with satellite data | |
| Schläpfer et al. | Evaluation of brefcor BRDF effects correction for HYSPEX, CASI, and APEX imaging spectroscopy data | |
| CN114113000B (en) | Inversion method, system and equipment for improving spectral reflectivity based on empirical linear model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |