Method and device for correcting BRDF effect of high-spatial-resolution optical satellite remote sensing image

Abstract

The invention provides a method and device for correcting the BRDF effect of a high-spatial-resolution optical satellite remote sensing image, and relates to the technical field of remote sensing image radiation unification. The method comprises the following steps: constructing a sample pixel library according to a preset land cover type data product and a preset sensor image data set to be subjected to BRDF effect correction, and determining an optimal BRDF prototype of sample pixels; according to the optimal BRDF prototype of the sample pixels and the spectral features, constructing a preset high-precision BRDF parameter lookup table of the image pixels of the sensor to be subjected to BRDF effect correction; and carrying out BRDF effect correction processing on a preset observation image of the sensor to be subjected to BRDF effect correction by adopting the constructed high-precision BRDF parameter lookup table to obtain an image after BRDF effect correction. By adopting the method, the BRDF effect correction of the high-spatial-resolution remote sensing image can be automatically realized with high precision, the surface reflectance difference caused by the observation geometric difference between the high-spatial-resolution remote sensing image data is effectively reduced, and the production of high-quality satellite remote sensing data products is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image radiation uniformity processing, and in particular to a method and device for correcting BRDF effect of high spatial resolution optical satellite remote sensing images. Background Art

[0002] Satellite remote sensing has the advantages of large monitoring range, fast data acquisition, strong visualization and large amount of information, and has become an important means of earth observation. In the context of global climate change and environmental degradation, optical satellite remote sensing images are of great significance for the dynamic monitoring of the earth's surface. With the development of global earth observation technology, more and more optical satellite remote sensing images have begun to emerge. Optical satellites receive the reflection of the earth's surface to the sun's incident light to form image data. The observed surface directional reflectivity is determined by the bidirectional reflectance distribution function BRDF, and its observed value is related to the sun's incident light-satellite observation angle. For the same observation target, the BRDF effect caused by the change in the geometric relationship between the sun's incident light and the satellite observation will cause differences in the radiant brightness observed by the sensor. The above differences will eventually be transmitted to the data fusion of different observation systems or the consistency of the long-term observation data products of the same observation system, resulting in the inability to maintain data continuity in space and time, which introduces uncertainty to quantitative remote sensing focusing on surface changes. Therefore, the change in surface reflectivity caused by the BRDF effect cannot be ignored. Correcting the BRDF effect of high-spatial-resolution optical satellite remote sensing images with a spatial resolution of 10m-30m or meter-scale can improve the radiation consistency between data, help promote the construction of virtual constellations and conduct constellation-based inversion, and also facilitate the application of downstream data products. For example, it is important to ensure food security and promote sustainable agricultural development. It has become a hot spot and frontier in quantitative remote sensing research.

[0003] At present, the BRDF effect correction methods for high spatial resolution optical satellite remote sensing images include: methods based on empirical estimation, methods combining medium and low resolution reflectance data, and methods based on low resolution BRDF prior knowledge. Among them, the method based on empirical estimation relies on calculating the gradient of directional reflectance change caused by the change of observed zenith angle. This method is simple and easy to implement, but it has the following problems: 1) The surface is difficult to be completely uniform. The change in pixel directional reflectance caused by each degree change of the observed zenith angle in a scene image is not necessarily fixed; 2) Cloudy and rainy weather will cause the loss of effective observation data, so that the angle of the observed zenith angle in the image cannot be fully sampled in space, further leading to inaccurate change gradients. In order to make up for the shortcomings of the empirical estimation method, scholars obtain high spatial resolution pixel BRDF parameters by combining medium and low resolution reflectance data, and then realize the BRDF effect correction of high spatial resolution images based on BRDF parameters. However, this method needs to rely on high-quality low-resolution data, and it is difficult to ensure the accuracy of the obtained high spatial resolution pixel BRDF parameters, resulting in unstable BRDF effect correction accuracy. In order to solve the problem of obtaining BRDF information of high spatial resolution pixels, methods for correcting BRDF effects of high spatial resolution images based on low-resolution BRDF prior knowledge have gradually been proposed. This method assumes that the BRDF changes obtained by the kernel-driven model match the actual observed BRDF changes, and inverts the BRDF parameters of high spatial resolution pixels based on low-resolution BRDF prior knowledge. The BRDF parameters are combined with the kernel-driven model to calculate the angle normalization scale factor, and the angle normalization scale factor is used to complete the BRDF effect correction of high spatial resolution images. The correction accuracy of this method depends on the accuracy of the BRDF parameters of the high spatial resolution pixels obtained, but the existing methods still face challenges in accurately obtaining the BRDF parameters of high spatial resolution pixels, resulting in limited accuracy in BRDF effect correction. Summary of the invention

[0004] In order to solve the problem in the prior art that it is difficult to accurately obtain the BRDF parameters of high spatial resolution optical satellite remote sensing images, resulting in limited BRDF effect correction accuracy, the embodiment of the present invention provides a method and device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images. The technical solution is as follows:

[0005] On the one hand, a method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images is provided, the method being implemented by a device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images, the method comprising:

[0006] S1. Construct a sample pixel library based on a preset land cover type data product and a preset sensor image data set to be corrected for BRDF effect, and determine the optimal BRDF prototype of the sample pixel;

[0007] S2, constructing a preset high-precision BRDF parameter lookup table for sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels;

[0008] S3. Use the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect, and obtain the image after BRDF effect correction.

[0009] Optionally, the step S1 constructs a sample pixel library according to a preset land cover type data product and a preset sensor image data set to be corrected for BRDF effect, and determines an optimal BRDF prototype of the sample pixel, including:

[0010] S11. In order to ensure the spatial representativeness of the land cover types of sample pixels, sample areas with rich land cover types are screened globally by referring to the preset land cover type products combined with visual interpretation;

[0011] S12. Considering that the surface may change over time, the preset sensor image data set to be corrected for BRDF effect is sampled at different times of the year in the selected sample area, and for the sample area seriously polluted by clouds and cloud shadows, observation data of many years are used as supplement;

[0012] S13, performing quality control on the image acquired above to remove low-quality observation pixels, and performing pixel sampling on the image data after quality control to obtain initial sample pixels;

[0013] S14, according to the obtained initial sample pixels, select reference pixels in the image data set after quality control that are at the same position as the sample pixels and observed at adjacent times; perform filtering processing according to the reflectivity difference threshold, remove the initial sample pixels that do not meet the filtering requirements, and use the remaining sample pixels to construct a sample pixel library; wherein the sample pixels and reference pixels at the same position that meet the filtering requirements constitute pixel pairs;

[0014] S15. Use the RTLSR kernel-driven model and the BRDF prototype to calculate the angle normalization scale factor, correct the sample pixel to the observation geometry consistent with the reference pixel, and define the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value as the optimal BRDF prototype of the sample pixel.

[0015] Optionally, the process of performing filtering according to the reflectivity difference threshold is expressed by the following formula (1)-formula (2):

[0016] (1)

[0017] (2)

[0018] in, Represents the apparent reflectance of the reference pixel in the blue light band; represents the apparent reflectance of the sample pixel in the blue light band, Indicates that the reference pixel is Band surface reflectivity; Indicates that the sample pixel is Band surface reflectivity.

[0019] Optionally, the process of using the RTLSR kernel driven model and the BRDF prototype to calculate the angle normalization scale factor, correcting the sample pixel to an observation geometry consistent with the reference pixel, and defining the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value as the optimal BRDF prototype of the sample pixel is expressed by the following formula (3)-formula (5):

[0020]

[0021]

[0022]

[0023] in, represents the volume scattering kernel; represents the geometrical optics kernel; Indicated in BRDF parameters of the ith BRDF prototype in the band,

[0024] Represents the observation zenith angle of the reference pixel; The solar zenith angle of the reference pixel; Indicates the relative azimuth of the reference pixel; Represents the observation zenith angle of the sample pixel; Represents the solar zenith angle of the sample pixel; Indicates the relative azimuth of the sample pixel; Indicated in The band uses the angle normalized scaling factor obtained from the i-th BRDF prototype, Indicates that the sample pixel is Observed values ​​of the band;

[0025] Indicates that the sample pixel is The observed value of the band uses the pixel value after the BRDF effect is corrected using the i-th BRDF prototype.

[0026] Optionally, the step S2 constructs a preset high-precision BRDF parameter lookup table of sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels, including:

[0027] S21, calculating the NDVI of the pixels in the sample pixel library, using the surface reflectance and NDVI of the sample pixels to construct a feature vector, clustering the sample pixels to obtain a clustering result; and obtaining a clustering label of the sample pixels according to the clustering result;

[0028] S22. Construct a preset high-precision BRDF parameter lookup table for sensor image pixels to be corrected for BRDF effects based on the cluster labels of the sample pixels and the optimal BRDF prototypes of the sample pixels.

[0029] Optionally, the preset high-precision BRDF parameter lookup table of sensor image pixels to be corrected for BRDF effect is expressed by the following formula (6):

[0030] (6)

[0031] in, It represents the weight of the i-th BRDF prototype, which is numerically equal to the proportion of the optimal BRDF prototype in the c-th type of pixels belonging to the i-th BRDF prototype. Represents the calculated BRDF parameters of the c-th type of pixel.

[0032] Optionally, the step S3 uses the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect to obtain the image after BRDF effect correction, including:

[0033] S31, performing quality control on the preset sensor observation image to be corrected for BRDF effect, removing low-quality observation pixels, and calculating the NDVI of the quality-controlled pixels;

[0034] S32, based on the cluster label dictionary, determine the pixel category through the surface reflectance and NDVI of the observed image pixel after quality control;

[0035] S33. According to the pixel category of the observed image after quality control, the corresponding BRDF parameter is selected in the constructed high-precision BRDF parameter lookup table, and the angle normalization scale factor is calculated using the RTLSR kernel-driven model to obtain the image data after BRDF effect correction, which is expressed by the following formula (7)-formula (8):

[0036]

[0037]

[0038] in, Indicates the target observation zenith angle; represents the target solar zenith angle; Indicates the relative azimuth of the target; Represents the observed zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the solar zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the relative azimuth of the pixel to be corrected for BRDF effect after quality control; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Angular normalization scale factor for the band, It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Observed values ​​of the band; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. The pixel value of the band after BRDF effect correction.

[0039] On the other hand, a device for correcting the BRDF effect of a high spatial resolution optical satellite remote sensing image is provided, and the device is applied to a method for correcting the BRDF effect of a high spatial resolution optical satellite remote sensing image, and the device comprises:

[0040] A construction and determination unit, used to construct a sample pixel library according to a preset surface cover type data product and a preset sensor image data set to be corrected for BRDF effect, and determine the optimal BRDF prototype of the sample pixel;

[0041] A construction unit, used to construct a high-precision BRDF parameter lookup table for preset sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels;

[0042] The correction unit is used to use the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect, so as to obtain the image after BRDF effect correction.

[0043] Optionally, the constructing and determining unit is used to:

[0044] In order to ensure the spatial representativeness of the land cover types of sample pixels, the preset land cover type products combined with visual interpretation were used to screen out sample areas with rich land cover types on a global scale.

[0045] Considering that the surface may change over time, the preset sensor image dataset to be corrected for BRDF effect is sampled at different times of the year in the selected sample area, and for the sample area seriously polluted by clouds and cloud shadows, observation data of many years are used as supplement;

[0046] Performing quality control on the images obtained above to remove low-quality observation pixels, and performing pixel sampling on the image data after quality control to obtain initial sample pixels;

[0047] According to the obtained initial sample pixels, reference pixels that are at the same position as the sample pixels and observed at adjacent times are selected from the image data set after quality control; filtering is performed according to the reflectivity difference threshold, the initial sample pixels that do not meet the filtering requirements are removed, and the remaining sample pixels are used to construct a sample pixel library; wherein the sample pixels and reference pixels at the same position that meet the filtering requirements constitute pixel pairs;

[0048] The RTLSR kernel-driven model and BRDF prototype are used to calculate the angle normalization scale factor, and the sample pixel is corrected to the observation geometry consistent with the reference pixel. The BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value is defined as the optimal BRDF prototype of the sample pixel.

[0049] Optionally, the process of performing filtering according to the reflectivity difference threshold is expressed by the following formula (1)-formula (2):

[0050] (1)

[0051] (2)

[0052] in, Represents the apparent reflectance of the reference pixel in the blue light band; represents the apparent reflectance of the sample pixel in the blue light band, Indicates that the reference pixel is Band surface reflectivity; Indicates that the sample pixel is Band surface reflectivity.

[0053] Optionally, the process of using the RTLSR kernel driven model and the BRDF prototype to calculate the angle normalization scale factor, correcting the sample pixel to an observation geometry consistent with the reference pixel, and defining the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value as the optimal BRDF prototype of the sample pixel is expressed by the following formula (3)-formula (5):

[0054]

[0055]

[0056]

[0057] in, represents the volume scattering kernel; represents the geometrical optics kernel; Indicated in BRDF parameters of the ith BRDF prototype in the band,

[0058] Represents the observation zenith angle of the reference pixel; The solar zenith angle of the reference pixel; Indicates the relative azimuth of the reference pixel; Represents the observation zenith angle of the sample pixel; Represents the solar zenith angle of the sample pixel; Indicates the relative azimuth of the sample pixel; Indicated in The band uses the angle normalized scaling factor obtained from the i-th BRDF prototype, Indicates that the sample pixel is Observed values ​​of the band;

[0059] Indicates that the sample pixel is The observed value of the band uses the pixel value after the BRDF effect is corrected using the i-th BRDF prototype.

[0060] Optionally, the construction unit is used to:

[0061] Calculate the NDVI of the pixels in the sample pixel library, use the surface reflectance and NDVI of the sample pixels to construct a feature vector, cluster the sample pixels to obtain clustering results; according to the clustering results, obtain the clustering labels of the sample pixels;

[0062] According to the clustering labels of the sample pixels and the optimal BRDF prototypes of the sample pixels, a high-precision BRDF parameter lookup table for the preset sensor image pixels to be corrected for BRDF effects is constructed.

[0063] Optionally, the preset high-precision BRDF parameter lookup table of sensor image pixels to be corrected for BRDF effect is expressed by the following formula (6):

[0064] (6)

[0065] in, It represents the weight of the i-th BRDF prototype, which is numerically equal to the proportion of the optimal BRDF prototype in the c-th type of pixels belonging to the i-th BRDF prototype. Represents the calculated BRDF parameters of the c-th type of pixel.

[0066] Optionally, the correction unit is used to:

[0067] Performing quality control on the preset sensor observation image to be corrected for BRDF effect, removing low-quality observation pixels, and calculating the NDVI of the pixels after quality control;

[0068] Based on the cluster label dictionary, the pixel category is determined by the surface reflectance and NDVI of the observed image pixels after quality control;

[0069] According to the pixel category of the observed image after quality control, the corresponding BRDF parameters are selected in the constructed high-precision BRDF parameter lookup table, and the RTLSR kernel-driven model is used to calculate the angle normalization scale factor to obtain the image data after BRDF effect correction, which is expressed by the following formula (7)-formula (8):

[0070]

[0071]

[0072] in, Indicates the target observation zenith angle; represents the target solar zenith angle; Indicates the relative azimuth of the target; Represents the observed zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the solar zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the relative azimuth of the pixel to be corrected for BRDF effect after quality control; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Angular normalization scale factor for the band, It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Observed values ​​of the band; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. The pixel value of the band after BRDF effect correction.

[0073] On the other hand, a device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images is provided, the device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images comprising: a processor; a memory, the memory having computer-readable instructions stored thereon, and when the computer-readable instructions are executed by the processor, any one of the methods for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images as described above is implemented.

[0074] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images.

[0075] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0076] The embodiment of the present invention first constructs a sample pixel library according to a preset surface cover type data product and a preset sensor image data set to be corrected for BRDF effects, and determines the optimal BRDF prototype of the sample pixels; constructs a high-precision BRDF parameter lookup table of the preset sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels; finally, uses the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effects, and obtains the image after BRDF effect correction.

[0077] The embodiment of the present invention can accurately reflect the BRDF shape changes of high spatial resolution pixels through the optimal BRDF prototype; combining the spectral characteristics of the high spatial resolution scale and the optimal BRDF prototype can construct a high-precision BRDF parameter lookup table, and the high-precision BRDF parameter lookup table can automatically and accurately correct the BRDF effect of high spatial resolution optical satellite remote sensing images, thereby promoting the production of high-quality satellite remote sensing data products. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 It is a flow chart of a method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images provided by an embodiment of the present invention;

[0080] Figure 2 It is a structural schematic diagram of a method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images provided by an embodiment of the present invention;

[0081] Figure 3 It is a block diagram of a device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images provided by an embodiment of the present invention;

[0082] Figure 4It is a schematic diagram of the structure of a device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0084] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the word "exemplary" is used to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0085] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0086] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0087] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0088] The embodiment of the present invention provides a method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images. The method can be implemented by a device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images. The device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images can be a terminal or a server. Figure 1 The flowchart of the method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images is shown in the figure. The processing flow of the method may include the following steps:

[0089] S1. Construct a sample pixel library based on the preset surface cover type data product and the preset sensor image data set to be corrected for BRDF effect, and determine the optimal BRDF prototype of the sample pixel.

[0090] Optionally, the specific implementation process of S1 may include S11-S15:

[0091] S11. In order to ensure the spatial representativeness of the land cover types of sample pixels, sample areas with rich land cover types are screened globally by referring to the preset land cover type products combined with visual interpretation;

[0092] S12. Considering that the surface may change over time, the preset sensor image data set to be corrected for BRDF effect is sampled at different times of the year in the selected sample area, and for the sample area seriously polluted by clouds and cloud shadows, observation data of many years are used as supplement;

[0093] S13, performing quality control on the image acquired above to remove low-quality observation pixels, and performing pixel sampling on the image data after quality control to obtain initial sample pixels;

[0094] S14, according to the obtained initial sample pixels, select reference pixels in the image data set after quality control that are at the same position as the sample pixels and observed at adjacent times; perform filtering processing according to the reflectivity difference threshold, remove the initial sample pixels that do not meet the filtering requirements, and use the remaining sample pixels to construct a sample pixel library; wherein the sample pixels and reference pixels at the same position that meet the filtering requirements constitute pixel pairs;

[0095] Optionally, the process of performing filtering according to the reflectivity difference threshold is expressed by the following formula (1)-formula (2):

[0096] (1)

[0097] (2)

[0098] in, Represents the apparent reflectance of the reference pixel in the blue light band; represents the apparent reflectance of the sample pixel in the blue light band, Indicates that the reference pixel is Band surface reflectivity; Indicates that the sample pixel is Band surface reflectivity.

[0099] Among them, because the blue light band has a short wavelength, it is sensitive to atmospheric influences. Formula (1) can be used to filter out pixel pairs whose pixel value differences are greater than the differences caused by atmospheric influences. Formula (2) can be used to perform similar filtering on the surface reflectance of each band of pixels to exclude pixels that may still have changes. The preset threshold is relatively strict, which can make the surface attributes of the remaining sample pixels after filtering as close as possible to the reference pixels.

[0100] S15. Use the RTLSR kernel-driven model and the BRDF prototype to calculate the angle normalization scale factor, correct the sample pixel to the observation geometry consistent with the reference pixel, and define the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value as the optimal BRDF prototype of the sample pixel.

[0101] Among them, the solar incidence-satellite observation angle of the reference pixel is taken as the benchmark, the BRDF prototype and the kernel-driven model are used to calculate the angle normalization scale factor, the pixel value of the sample pixel is corrected to the solar incidence-satellite observation geometric conditions consistent with the reference pixel, and the difference in surface reflectance of the pixel pair before and after BRDF effect correction is used as the evaluation index to evaluate the correction result of each BRDF prototype, and the prototype with the smallest difference in surface reflectance of the pixel pair after correction is determined as the optimal BRDF prototype for the sample pixel.

[0102] Optionally, the process of using the RTLSR kernel driven model and the BRDF prototype to calculate the angle normalization scale factor, correcting the sample pixel to an observation geometry consistent with the reference pixel, and defining the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value as the optimal BRDF prototype of the sample pixel is expressed by the following formula (3)-formula (5):

[0103]

[0104]

[0105]

[0106] in, represents the volume scattering kernel; represents the geometrical optics kernel; Indicated in BRDF parameters of the ith BRDF prototype in the band,

[0107] Represents the observation zenith angle of the reference pixel; The solar zenith angle of the reference pixel; Indicates the relative azimuth of the reference pixel; Represents the observation zenith angle of the sample pixel; Represents the solar zenith angle of the sample pixel; Indicates the relative azimuth of the sample pixel; Indicated in The band uses the angle normalized scaling factor obtained from the i-th BRDF prototype, Indicates that the sample pixel is Observed values ​​of the band; Indicates that the sample pixel is The observed value of the band uses the pixel value after the BRDF effect is corrected using the i-th BRDF prototype.

[0108] S2. According to the optimal BRDF prototype and spectral characteristics of the sample pixels, a high-precision BRDF parameter lookup table of the preset sensor image pixels to be corrected for BRDF effects is constructed.

[0109] Optionally, the specific implementation process of S2 may include S21-S22:

[0110] S21, calculating the NDVI of the pixels in the sample pixel library, using the surface reflectance and NDVI of the sample pixels to construct a feature vector, clustering the sample pixels to obtain a clustering result; and obtaining a clustering label of the sample pixels according to the clustering result;

[0111] S22. Construct a preset high-precision BRDF parameter lookup table for sensor image pixels to be corrected for BRDF effects based on the cluster labels of the sample pixels and the optimal BRDF prototypes of the sample pixels.

[0112] In a feasible implementation, the NDVI and surface reflectance of high spatial resolution pixels are used to construct a feature vector, and the filtered sample pixel samples are clustered based on the feature vector to obtain cluster labels of the sample pixels; the composition of the optimal BRDF prototype of each type of sample pixels is counted, and the weight of each BRDF prototype in each type of high spatial resolution pixels is determined according to the composition; based on the weight of each BRDF prototype and the corresponding BRDF parameters, the BRDF parameters of each type of sample pixels are weightedly calculated, and a high spatial resolution pixel BRDF parameter lookup table is constructed according to the cluster labels.

[0113] Optionally, the preset high-precision BRDF parameter lookup table of the sensor image pixel to be corrected for BRDF effect is expressed by the following formula (6):

[0114] (6)

[0115] in, It represents the weight of the i-th BRDF prototype, which is numerically equal to the proportion of the optimal BRDF prototype in the c-th type of pixels belonging to the i-th BRDF prototype. Represents the calculated BRDF parameters of the c-th type of pixel.

[0116] S3. Use the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect, and obtain the image after BRDF effect correction.

[0117] Optionally, the specific implementation of S3 may include S31-S33:

[0118] S31, performing quality control on the preset sensor observation image to be corrected for BRDF effect, removing low-quality observation pixels, and calculating the NDVI of the pixels after quality control;

[0119] S32, based on the cluster label dictionary, determine the pixel category through the surface reflectance and NDVI of the observed image pixel after quality control;

[0120] S33. According to the pixel category of the observed image after quality control, the corresponding BRDF parameter is selected in the constructed high-precision BRDF parameter lookup table, and the angle normalization scale factor is calculated using the RTLSR kernel-driven model to obtain the image data after BRDF effect correction, which is expressed by the following formula (7)-formula (8):

[0121]

[0122]

[0123] in, Indicates the target observation zenith angle; represents the target solar zenith angle; Indicates the relative azimuth of the target; Represents the observed zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the solar zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the relative azimuth of the pixel to be corrected for BRDF effect after quality control; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. The angle normalization scale factor of the band, It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Observed values ​​of the band; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. The pixel values ​​of the band after BRDF effect correction.

[0124] Among them, Figure 2The figure shows a structural schematic diagram of a correction of BRDF effect of high spatial resolution optical satellite remote sensing image provided by an embodiment of the present invention; in a feasible implementation manner, a surface cover type data set and a high spatial resolution reflectance data set to be corrected for BRDF effect are obtained, sample pixels and reference pixels are selected based on the above data sets to form pixel pairs; the surface reflectance of the sample pixels is corrected to an observation geometry consistent with that of the reference pixels using the RTLSR kernel-driven model combined with the BRDF prototype, and the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value is defined as the optimal BRDF prototype of the sample pixel by evaluating the correction results using each BRDF prototype; the NDVI of the sample pixel is calculated, a feature vector is constructed using the surface reflectance of the sample pixel and the NDVI, the sample pixels are clustered, and a clustering result is obtained; based on the clustering result, the sample pixels are obtained. cluster labels of sample pixels; construct a high-precision BRDF parameter lookup table for high-spatial-resolution image pixels to be corrected for BRDF effects based on the cluster labels of sample pixels and the optimal BRDF prototypes of sample pixels; obtain the high-spatial-resolution image to be corrected for BRDF effects; calculate the pixel NDVI based on the high-spatial-resolution surface reflectance image to be corrected for BRDF effects, and determine the pixel BRDF parameters using the constructed BRDF parameter lookup table combined with the surface reflectance and NDVI of the pixel; calculate the angle normalization scale factor using the RTLSR driven model based on the observed angle geometry information, BRDF parameters of the high-spatial-resolution image pixels to be corrected for BRDF effects, and the target angle geometry information as a benchmark; complete the BRDF effect correction of the high-spatial-resolution surface reflectance image based on the angle normalization scale factor, and obtain the surface reflectance image corrected for BRDF effects.

[0125] The embodiment of the present invention first constructs a sample pixel library according to a preset surface cover type data product and a preset sensor image data set to be corrected for BRDF effects, and determines the optimal BRDF prototype of the sample pixels; constructs a high-precision BRDF parameter lookup table of the preset sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels; finally, uses the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effects, and obtains the image after BRDF effect correction.

[0126] The embodiment of the present invention can accurately reflect the BRDF shape changes of high spatial resolution pixels through the optimal BRDF prototype; combining the spectral characteristics of the high spatial resolution scale and the optimal BRDF prototype can construct a high-precision BRDF parameter lookup table, and the high-precision BRDF parameter lookup table can automatically and accurately correct the BRDF effect of high spatial resolution optical satellite remote sensing images, thereby promoting the production of high-quality satellite remote sensing data products.

[0127] Figure 3 The present invention is a block diagram of a device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images according to an exemplary embodiment. The device is used for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images. Figure 3 The device includes a construction and determination unit 310, a construction unit 320 and a correction unit 330. Among them:

[0128] A construction and determination unit 310 is used to construct a sample pixel library according to a preset land cover type data product and a preset sensor image data set to be corrected for BRDF effect, and determine an optimal BRDF prototype of the sample pixel;

[0129] A construction unit 320, configured to construct a high-precision BRDF parameter lookup table for preset sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels;

[0130] The correction unit 330 is used to use the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect, so as to obtain the image after BRDF effect correction.

[0131] Optionally, the constructing and determining unit 310 is used to:

[0132] In order to ensure the spatial representativeness of the land cover types of sample pixels, the preset land cover type products combined with visual interpretation were used to screen out sample areas with rich land cover types on a global scale.

[0133] Considering that the surface may change over time, the preset sensor image dataset to be corrected for BRDF effect is sampled at different times of the year in the selected sample area, and for the sample area seriously polluted by clouds and cloud shadows, observation data of many years are used as supplement;

[0134] Performing quality control on the images obtained above to remove low-quality observation pixels, and performing pixel sampling on the image data after quality control to obtain initial sample pixels;

[0135] According to the obtained initial sample pixels, reference pixels that are at the same position as the sample pixels and observed at adjacent times are selected from the image data set after quality control; filtering is performed according to the reflectivity difference threshold, the initial sample pixels that do not meet the filtering requirements are removed, and the remaining sample pixels are used to construct a sample pixel library; wherein the sample pixels and reference pixels at the same position that meet the filtering requirements constitute pixel pairs;

[0136] The RTLSR kernel-driven model and BRDF prototype are used to calculate the angle normalization scale factor, and the sample pixel is corrected to the observation geometry consistent with the reference pixel. The BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value is defined as the optimal BRDF prototype of the sample pixel.

[0137] Optionally, the process of performing filtering according to the reflectivity difference threshold is expressed by the following formula (1)-formula (2):

[0138] (1)

[0139] (2)

[0140] in, Represents the apparent reflectance of the reference pixel in the blue light band; represents the apparent reflectance of the sample pixel in the blue light band, Indicates that the reference pixel is Band surface reflectivity; Indicates that the sample pixel is Band surface reflectivity.

[0141] Optionally, the process of using the RTLSR kernel driven model and the BRDF prototype to calculate the angle normalization scale factor, correcting the sample pixel to an observation geometry consistent with the reference pixel, and defining the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value as the optimal BRDF prototype of the sample pixel is expressed by the following formula (3)-formula (5):

[0142]

[0143]

[0144] (5)

[0145] in, represents the volume scattering kernel; represents the geometrical optics kernel; Indicated in BRDF parameters of the ith BRDF prototype in the band,

[0146] Represents the observation zenith angle of the reference pixel; The solar zenith angle of the reference pixel; Indicates the relative azimuth of the reference pixel; Represents the observation zenith angle of the sample pixel; Represents the solar zenith angle of the sample pixel; Indicates the relative azimuth of the sample pixel; Indicated in The band uses the angle normalized scaling factor obtained from the i-th BRDF prototype, Indicates that the sample pixel is Observed values ​​of the band; Indicates that the sample pixel is The observed value of the band uses the pixel value after the BRDF effect is corrected using the i-th BRDF prototype.

[0147] Optionally, the construction unit 320 is used to:

[0148] Calculate the NDVI of the pixels in the sample pixel library, use the surface reflectance and NDVI of the sample pixels to construct a feature vector, cluster the sample pixels to obtain clustering results; according to the clustering results, obtain the clustering labels of the sample pixels;

[0149] According to the clustering labels of the sample pixels and the optimal BRDF prototypes of the sample pixels, a high-precision BRDF parameter lookup table for the preset sensor image pixels to be corrected for BRDF effects is constructed.

[0150] Optionally, the preset high-precision BRDF parameter lookup table of sensor image pixels to be corrected for BRDF effect is expressed by the following formula (6):

[0151] (6)

[0152] in, It represents the weight of the i-th BRDF prototype, which is numerically equal to the proportion of the optimal BRDF prototype in the c-th type of pixels belonging to the i-th BRDF prototype. Represents the calculated BRDF parameters of the c-th type of pixel.

[0153] Optionally, the correction unit 330 is configured to:

[0154] Performing quality control on the preset sensor observation image to be corrected for BRDF effect, removing low-quality observation pixels, and calculating the NDVI of the pixels after quality control;

[0155] Based on the cluster label dictionary, the pixel category is determined by the surface reflectance and NDVI of the observed image pixels after quality control;

[0156] According to the pixel category of the observed image after quality control, the corresponding BRDF parameters are selected in the constructed high-precision BRDF parameter lookup table, and the RTLSR kernel-driven model is used to calculate the angle normalization scale factor to obtain the image data after BRDF effect correction, which is expressed by the following formula (7)-formula (8):

[0157]

[0158]

[0159] in, Indicates the target observation zenith angle; represents the target solar zenith angle; Indicates the relative azimuth of the target; Represents the observed zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the solar zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the relative azimuth of the pixel to be corrected for BRDF effect after quality control; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Angular normalization scale factor for the band, represents the observed value of the pixel to be corrected for BRDF effect belonging to the cth category in the λ band after quality control; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. The pixel value of the band after BRDF effect correction.

[0160] The embodiment of the present invention first constructs a sample pixel library according to a preset surface cover type data product and a preset sensor image data set to be corrected for BRDF effects, and determines the optimal BRDF prototype of the sample pixels; constructs a high-precision BRDF parameter lookup table of the preset sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels; finally, uses the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effects, and obtains the image after BRDF effect correction.

[0161] The embodiment of the present invention can accurately reflect the BRDF shape changes of high spatial resolution pixels through the optimal BRDF prototype; combining the spectral characteristics of the high spatial resolution scale and the optimal BRDF prototype can construct a high-precision BRDF parameter lookup table, and the high-precision BRDF parameter lookup table can automatically and accurately correct the BRDF effect of high spatial resolution optical satellite remote sensing images, thereby promoting the production of high-quality satellite remote sensing data products.

[0162] Figure 4 is a schematic diagram of the structure of a device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images provided by an embodiment of the present invention, such as Figure 4 As shown, the device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images may include the above Figure 3The device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images shown in FIG. 40A . Optionally, the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images may include a first processor 2001 .

[0163] Optionally, the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images may also include a memory 2002 and a transceiver 2003 .

[0164] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0165] Combine the following Figure 4 The components of the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images are specifically introduced:

[0166] The first processor 2001 is the control center of the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0167] Optionally, the first processor 2001 can perform various functions of the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0168] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0169] In a specific implementation, as an embodiment, the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images may also include multiple processors, such as Figure 4The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0170] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0171] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0172] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0173] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0174] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 4(not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0175] It should be noted that Figure 4 The structure of the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0176] In addition, the technical effects of the device 410 for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images can refer to the technical effects of the method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images described in the above method embodiment, and will not be repeated here.

[0177] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0178] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0179] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0180] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0181] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0182] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0183] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0184] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0185] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0186] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0188] If the functions are implemented in the form of software functional units and sold or adopted as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0189] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images, characterized in that: The method comprises: S1. Construct a sample pixel library based on a preset land cover type data product and a preset sensor image data set to be corrected for BRDF effect, and determine the optimal BRDF prototype of the sample pixel; S2, constructing a preset high-precision BRDF parameter lookup table for sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels; S3. Use the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect, and obtain the image after BRDF effect correction.

2. The method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images according to claim 1, characterized in that: The S1 constructs a sample pixel library according to a preset surface cover type data product and a preset sensor image data set to be corrected for BRDF effect, and determines the optimal BRDF prototype of the sample pixel, including: S11. In order to ensure the spatial representativeness of the land cover types of sample pixels, sample areas with rich land cover types are screened globally by referring to the preset land cover type products combined with visual interpretation; S12. Considering that the surface may change over time, the preset sensor image data set to be corrected for BRDF effect is sampled at different times of the year in the selected sample area, and for the sample area seriously polluted by clouds and cloud shadows, observation data of many years are used as supplement; S13, performing quality control on the image acquired above to remove low-quality observation pixels, and performing pixel sampling on the image data after quality control to obtain initial sample pixels; S14, according to the obtained initial sample pixels, select reference pixels in the image data set after quality control that are at the same position as the sample pixels and observed at adjacent times; perform filtering processing according to the reflectivity difference threshold, remove the initial sample pixels that do not meet the filtering requirements, and use the remaining sample pixels to construct a sample pixel library; wherein the sample pixels and reference pixels at the same position that meet the filtering requirements constitute pixel pairs; S15. Use the RTLSR kernel-driven model and the BRDF prototype to calculate the angle normalization scale factor, correct the sample pixel to the observation geometry consistent with the reference pixel, and define the BRDF prototype used when the pixel value of the corrected sample pixel is closest to the reference pixel observation value as the optimal BRDF prototype of the sample pixel.

3. The method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images according to claim 2, characterized in that: The process of filtering according to the reflectivity difference threshold is expressed by the following formula (1)-formula (2): （1） （2） in, It represents the apparent reflectance of the reference pixel in the blue light band; represents the apparent reflectance of the sample pixel in the blue light band, Indicates that the reference pixel is Band surface reflectivity; Indicates that the sample pixel is Band surface reflectivity.

4. The method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images according to claim 2, characterized in that: The process of using the RTLSR kernel driven model and the BRDF prototype to calculate the angle normalization scale factor and correcting the sample pixel to the observation geometry consistent with the reference pixel is expressed by the following formula (3)-formula (5): ； （4） （5） in, represents the volume scattering kernel; represents the geometrical optics kernel; Indicated in BRDF parameters of the ith BRDF prototype in the band, Represents the observation zenith angle of the reference pixel; The solar zenith angle of the reference pixel; Indicates the relative azimuth of the reference pixel; Represents the observation zenith angle of the sample pixel; Represents the solar zenith angle of the sample pixel; Indicates the relative azimuth of the sample pixel; Indicated in The band uses the angle normalized scaling factor obtained from the i-th BRDF prototype, Indicates that the sample pixel is Observed values ​​of the band; Indicates that the sample pixel is The observed value of the band uses the pixel value after the BRDF effect is corrected using the i-th BRDF prototype.

5. The method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images according to claim 1, characterized in that: The step S2 constructs a high-precision BRDF parameter lookup table for sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels, including: S21, calculating the NDVI of the pixels in the sample pixel library, using the surface reflectance and NDVI of the sample pixels to construct a feature vector, clustering the sample pixels to obtain a clustering result; and obtaining a clustering label of the sample pixels according to the clustering result; S22. Construct a preset high-precision BRDF parameter lookup table for sensor image pixels to be corrected for BRDF effects based on the cluster labels of the sample pixels and the optimal BRDF prototypes of the sample pixels.

6. The method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images according to claim 5, characterized in that: The preset high-precision BRDF parameter lookup table of the sensor image pixel to be corrected for BRDF effect is expressed by the following formula (6): （6） in, It represents the weight of the i-th BRDF prototype, which is numerically equal to the proportion of the optimal BRDF prototype in the c-th type of pixels belonging to the i-th BRDF prototype. Represents the calculated BRDF parameters of the c-th type of pixel.

7. The method for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images according to claim 1, characterized in that: The S3 uses the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect to obtain the image after BRDF effect correction, including: S31, performing quality control on the preset sensor observation image to be corrected for BRDF effect, removing low-quality observation pixels, and calculating the NDVI of the pixels after quality control; S32, based on the cluster label dictionary, determine the pixel category through the surface reflectance and NDVI of the observed image pixel after quality control; S33. According to the pixel category of the observed image after quality control, the corresponding BRDF parameter is selected in the constructed high-precision BRDF parameter lookup table, and the angle normalization scale factor is calculated using the RTLSR kernel-driven model to obtain the image data after BRDF effect correction, which is expressed by the following formula (7)-formula (8): ； ； in, Indicates the target observation zenith angle; represents the target solar zenith angle; Indicates the relative azimuth of the target; Represents the observed zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the solar zenith angle of the pixel to be corrected for BRDF effect after quality control; Represents the relative azimuth of the pixel to be corrected for BRDF effect after quality control; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Angular normalization scale factor for the band, It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. Observed values ​​of the band; It means that the pixels to be corrected for BRDF effect belong to the cth class after quality control. The pixel value of the band after BRDF effect correction.

8. A device for correcting the BRDF effect of a high spatial resolution optical satellite remote sensing image, wherein the device for correcting the BRDF effect of a high spatial resolution optical satellite remote sensing image is used to implement the method for correcting the BRDF effect of a high spatial resolution optical satellite remote sensing image as claimed in any one of claims 1 to 7, characterized in that: The device comprises: A construction and determination unit, used to construct a sample pixel library according to a preset surface cover type data product and a preset sensor image data set to be corrected for BRDF effect, and determine the optimal BRDF prototype of the sample pixel; A construction unit, used to construct a high-precision BRDF parameter lookup table for preset sensor image pixels to be corrected for BRDF effects according to the optimal BRDF prototype and spectral characteristics of the sample pixels; The correction unit is used to use the constructed high-precision BRDF parameter lookup table to perform BRDF effect correction processing on the preset sensor observation image to be corrected for BRDF effect, so as to obtain the image after BRDF effect correction.

9. A device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images, characterized in that: The device for correcting the BRDF effect of high spatial resolution optical satellite remote sensing images comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

Images