CN114778483A - Method for correcting terrain shadow of remote sensing image near-infrared wave band for monitoring mountainous region - Google Patents
Method for correcting terrain shadow of remote sensing image near-infrared wave band for monitoring mountainous region Download PDFInfo
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Abstract
The invention provides a method for correcting the terrain shadow of a remote sensing image near-infrared waveband for monitoring mountainous regions, which comprises the following steps: step S1: acquiring satellite remote sensing data and DEM data of a research area, and preprocessing the data; step S2: extracting a terrain shadow by adopting machine learning and water body index (NDWI); step S3: SCS + C terrain correction is carried out by adopting the earth surface reflectivity after atmospheric correction; step S4: calculating a topographic Shadow Elimination Vegetation Index (SEVI) by adopting the surface reflectivity; step S5: carrying out terrain shadow correction on red light wave band; step S6: and (5) performing near-infrared band terrain shadow correction. By applying the technical scheme, the interference of the terrain shadow on the near infrared wave band of the remote sensing image can be effectively eliminated, and the defects that the conventional terrain shadow correction method based on the DEM is poor in the correction effect of the terrain shadow on the near infrared wave band, and particularly in the shadow-drop failure are overcome.
Description
Technical Field
The invention relates to the technical field of terrain shadow compensation, in particular to a method for correcting terrain shadow of a remote sensing image near-infrared band for monitoring mountainous regions.
Background
The accuracy of monitoring, classifying and extracting the mountain land features is seriously interfered by the terrain shadow, and great difficulty is caused for interpreting the mountain land cover, so that the research on the terrain shadow spectral information compensation method has extremely important significance. The current terrain shading correction method mainly comprises an empirical correction model, a physical correction model, a wave band combination optimization calculation model (vegetation index) and the like based on DEM data. The experience correction method based on DEM data mainly considers direct solar radiation and carries out experience correction based on the statistical relationship between reflectivity and cos i, wherein the experience correction comprises a statistical-experience model, a normalization model, C correction, SCS correction and the like. The physical correction model considers direct solar radiation, atmospheric scattered radiation and surrounding terrain reflected radiation at the same time, the effect is good, but the model parameters are more, the calculation process is complex, and the popularization difficulty is high. The method for calculating the model based on the band combination optimization obtains vegetation information after eliminating the terrain influence by constructing a special vegetation index, such as a band ratio model, a VBSI index, a Terrain Adjusting Vegetation Index (TAVI), a normalized difference mountain vegetation index, an improved enhanced vegetation index, a Shadow Eliminating Vegetation Index (SEVI) and the like, but the model can only obtain index information of a single band and lacks multispectral information. The recently proposed "a method for correcting landform shadow in visible light band of remote sensing image (patent No. 2021101469628)" can correct red-green-blue visible light landform shadow, but cannot correct near infrared band landform shadow. The near-infrared band spectral information is important data for vegetation remote sensing and is important for monitoring mountain land features and classifying and extracting vegetation.
Disclosure of Invention
In view of the above, the present invention provides a method for correcting a terrain shadow in a near-infrared band of a remote sensing image for monitoring a terrain feature in a mountainous area, so as to obtain information of a reflectivity in the near-infrared band of the remote sensing image, which can effectively eliminate the interference of the terrain shadow.
In order to realize the purpose, the invention adopts the following technical scheme: a method for correcting the terrain shadow of a remote sensing image near-infrared band for monitoring mountainous regions comprises the following steps:
step S1: acquiring satellite remote sensing data and Digital Elevation Model (DEM) data of a research area, and performing data preprocessing;
step S2: extracting a terrain shadow by adopting machine learning and water body index (NDWI);
step S3: SCS + C terrain correction is carried out by adopting the earth surface reflectivity after atmospheric correction;
step S4: calculating a terrain Shadow Elimination Vegetation Index (SEVI) by adopting the surface reflectivity;
step S5: carrying out terrain shadow correction on red light wave band;
step S6: calculating the shadow near-infrared band reflectivity by using the shadow red band reflectivity according to the following formula:
ρnir-tc＝RVI·ρr-tc＝SEVI·ρr-tc (1)
in the formula, ρnir-tcCorrecting the reflectivity, rho, of the near infrared band in the shadow region for terrainr-t. The reflectivity of the red wave band in the shadow area after the terrain correction is carried out, and RVI is a specific vegetation index;
step S7: and (3) evaluating the correction effect, namely evaluating the terrain correction effect by adopting visual analysis, spectral statistical analysis, relative error analysis and the like, wherein the spectral statistical analysis specifically comprises reflectivity and cosi scattered point analysis and a rose diagram.
In a preferred embodiment, the step S1 is specifically:
step S11: collecting satellite remote sensing data and DEM data of a research area;
step S12: carrying out radiometric calibration and atmospheric correction on the satellite remote sensing data to obtain surface reflectivity data of the research area;
step S13: and performing projection transformation, resampling and cutting on the DEM data to obtain the DEM data of the research area, and finally standardizing the obtained data into the same data format.
In a preferred embodiment, the step S2 is specifically:
step S21: respectively selecting three types of sample points, namely a shadow area, an illumination area and a water body, in a research area;
step S22: and classifying the research area by combining a Random Forest (RF) classification method and three types of sample points, extracting the overall shadow, and further separating the interference of the water body by using the water body index NDWI.
In a preferred embodiment, the step S3 specifically includes:
step S31: and calculating a cosine value cos i of the solar incident angle by combining the DEM data of the research area and the solar altitude and solar azimuth data of the remote sensing image, wherein the calculation formula is as follows:
cos i＝cosσcosθ+sinσsinθcos(β-ω) (2)
wherein i represents a solar incident angle; sigma represents a terrain slope angle; theta represents the solar zenith angle; beta represents a terrain slope angle; ω represents the sun azimuth;
step S32: calculating the correction parameter c by combining cos i, wherein the calculation formula is as follows:
LT＝a+b×cosi (3)
c＝a/b (4)
in the formula, LTIs the surface reflectance value, a and b are the coefficients of the fitted linear equation;
step S33: SCS + C correction is carried out on the red light wave band earth surface reflectivity data, and the calculation formula is as follows:
in the formula, LSCS+CRepresenting SCS + C correctionThe subsequent pixel value; l is a radical of an alcoholTRepresenting the pixel values prior to SCS + C correction.
In a preferred embodiment, the step S4 specifically includes:
calculating the SEVI according to the surface reflectivity of the red light wave band and the near infrared wave band, wherein the formula is as follows:
in the formula, ρrFor the reflectance of the red band, p, of the remote-sensing imagenirF (delta) is an adjusting factor for the reflectivity of the near infrared band of the remote sensing image.
Wherein, f (delta) is calculated by the following steps: calculating a gradient map by using a DEM (digital elevation model), resampling the gradient map into blocks with 6 multiplied by 6 square kilometers of spatial resolution, and selecting the first 1% of blocks with the maximum gradient as a steep block set; calculating SEVI and the information entropy H thereof block by adopting the remote sensing image reflectivity data according to the space range of each steep block, wherein the formula is as follows:
in the formula, piThe ratio of the value of the i pixel SEVI to the sum of the SEVI of the steep slope blocks, n is the pixel number of the steep slope blocks, SEVIiTaking the SEVI value of the i pixel;
starting the adjustment factor f (delta) from 0, sequentially increasing by a preset step length of 0.001, calculating the SEVI information entropy of the steep block, and taking the adjustment factor f (delta) corresponding to the maximum H as the optimization solution f of the adjustment factor of the steep block when the value of H is maximumbThe formula is as follows:
fb＝argmax(H)，f(Δ)∈(0.001，1.000) (9)
comparing the information entropies of different steep blocks, and selecting the adjustment factor corresponding to the pure vegetation steep block with the largest information entropy as the wholeOptimal adjustment factor f of scene imageoptThe formula is as follows:
fopt＝arqmax(H)，fb∈(fbj，j＝1，...，m) (10)
wherein m is the number of the steep blocks.
In a preferred embodiment, the step S5 is specifically:
step S51: taking the whole research area as a processing range, generating a certain number of random points by adopting an ArcGIS random point generating tool, and extracting the random points by taking the illumination area layer as a mask;
step S52: recording the reflectivity of the corrected red light wave band corresponding to the random sample point of the illumination area in an attribute table of the point by adopting a multi-value extraction to-point tool in ArcGIS, making a sample set by combining the attribute table of the random sample point of the illumination area, and dividing the sample set into a training sample set and a testing sample set according to a preset proportion;
step S53: constructing an RF regression model by taking SEVI of the training sample set as an independent variable and taking the red light wave band earth surface reflectivity corrected by SCS + C of the training sample set as a dependent variable;
step S54: adjusting the hyper-parameters to optimize the RF regression model;
step S55: and (3) predicting the reflectivity of the red light wave band in the terrain shadow area by taking the SEVI in the terrain shadow area as an independent variable and adopting an RF regression model after parameter adjustment and optimization.
Compared with the prior art, the invention has the following beneficial effects:
the method can effectively eliminate the interference of the terrain shadow, particularly the falling shadow on the near infrared band of the remote sensing image, makes up the defect that SEVI can only obtain the single-band information for eliminating the interference of the terrain shadow, and also makes up the defect that the conventional terrain correction method based on DEM has poor effect in the shadow area, particularly the falling shadow is invalid.
Drawings
FIG. 1 is a schematic flow diagram of a method in accordance with a preferred embodiment of the present invention;
FIG. 2 is a comparison graph of the pre-and post-correction effects of the near-infrared band terrain shading in accordance with the preferred embodiment of the present invention;
FIG. 3 is a diagram of a spectral signature of the reflectivity in the near infrared band in accordance with a preferred embodiment of the present invention;
FIG. 4 is a histogram of the reflectivity error of the near infrared band terrain shadow (umbra, falling shadow) relative to the sunny slope reflectivity in accordance with a preferred embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application; as used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Referring to fig. 1-4, the invention provides a method for correcting a terrain shadow of a remote sensing image in a near infrared band for monitoring a mountain land, which comprises the following steps:
step S1: acquiring and preprocessing data, wherein the required data comprises: satellite telemetry data such as Landsat 8OLI, DEM data such as ASTER GDEM V2. Carrying out radiometric calibration and atmospheric correction on the satellite remote sensing data to obtain earth surface reflectivity data; performing operations such as projection transformation, resampling, cutting and the like on the GDEM V2 data to obtain DEM data, and finally normalizing the obtained data into the same data format;
step S2: extracting the terrain shadow, namely extracting the total shadow by RF classification, and then calculating NDWI to remove water in the shadow to obtain a terrain shadow extraction result;
step S3: SCS + C correction is carried out on the surface reflectivity;
step S4: carrying out SEVI calculation by adopting the surface reflectivity;
step S5: carrying out terrain shadow correction on red light wave bands;
step S6: calculating the shadow near-infrared band reflectivity according to the shadow red band reflectivity by adopting the following formula:
ρnir-tc＝RVI·ρr-tc＝SEVI·ρr-tc， (1)
in the formula, ρnir-tcCorrecting the reflectivity of the near infrared band of the shadow region for terrainr-tcAnd the RVI is a ratio vegetation index for the reflectance of the red wave band in the shadow area after terrain correction.
Step S7: the evaluation of the correction effect is performed by visual analysis, spectral statistical analysis (reflectance and cosi scattering analysis, rose diagram), relative error analysis, and the like.
In this embodiment, the step S1 specifically includes:
step S11: acquiring satellite remote sensing data and DEM data of a research area;
step S12: carrying out radiometric calibration and atmospheric correction on the satellite remote sensing data to obtain surface reflectivity data of the research area;
step S13: and performing projection transformation, resampling and cutting on the DEM data to obtain the DEM data of the research area, and finally normalizing the obtained data into the same data format.
In this embodiment, the step S2 specifically includes:
step S21: respectively selecting three types of sample points, namely a shadow area, an illumination area and a water body, in a research area;
step S22: classifying the research area by combining an RF classification method with three types of sample points, extracting a total shadow, and further separating the interference of the water body by using NDWI;
in this embodiment, the step S3 specifically includes:
step S31: and calculating a solar incident angle cosine value cos i by combining the DEM data of the research area and the solar altitude and solar azimuth data of the remote sensing image, wherein the calculation formula is as follows:
cos i＝cosσcosθ+sinσsinθcos(β-ω) (2)
wherein i represents a solar incident angle; sigma represents a terrain slope angle; theta represents the solar zenith angle; beta represents a terrain slope angle; ω represents the sun azimuth;
step S32: calculating the adjusting parameter c by combining cos i, wherein the calculation formula is as follows:
LT＝a+b×cosi (3)
c＝a/b (4)
in the formula, LTIs the surface reflectance value, a and b are the coefficients of the fitted linear equation;
step S33: SCS + C correction is carried out on the earth surface reflectivity data of the red light wave band, and the calculation formula is as follows:
in the formula, LSCS+CRepresenting the pixel value after SCS + C correction; l is a radical of an alcoholTRepresenting the pixel value before SCS + C correction; theta represents the solar zenith angle; sigma represents a terrain slope angle; c represents a terrain correction parameter; i represents the solar incident angle.
In this embodiment, step S4 specifically includes the following steps:
calculating the SEVI of the research area according to the surface reflectivity of the red light wave band and the near infrared wave band, wherein the formula is as follows:
in the formula, ρrFor the reflectance of the red band of the remote-sensing image, ρnirF (delta) is an adjusting factor for the reflectivity of the near infrared band of the remote sensing image.
Wherein, the f (delta) calculation steps are as follows: calculating a gradient map by using a DEM (digital elevation model), resampling the gradient map into blocks with 6 multiplied by 6 square kilometers of spatial resolution, and selecting the first 1% of blocks with the maximum gradient as a steep block set; calculating SEVI and the information entropy H thereof block by adopting the remote sensing image reflectivity data according to the space range of each steep block, wherein the formula is as follows:
in the formula, piThe ratio of the value of the i pixel SEVI to the sum of the SEVI of the steep slope blocks, n is the pixel number of the steep slope blocks, SEVIiTaking the SEVI value of the i pixel;
starting the adjustment factor f (delta) from 0, sequentially increasing by a preset step length of 0.001, calculating the SEVI information entropy of the steep block, and taking the adjustment factor f (delta) corresponding to the maximum H as the optimization solution f of the adjustment factor of the steep block when the value of H is maximumbThe formula is as follows:
fb＝argmax(H)，f(Δ)∈(0.001，1.000) (9)
comparing the information entropies of different steep blocks, and selecting the adjustment factor corresponding to the pure vegetation steep block with the largest information entropy as the optimal adjustment factor f of the whole scene imageoptThe formula is as follows:
fopt＝argmax(H)，fb∈(fbj，j＝1，...m) (10)
wherein m is the number of steep blocks.
In this embodiment, step S5 specifically includes the following steps:
step S51: taking the whole research area as a processing range, generating a certain number of random points by adopting an ArcGIS random point generating tool, and extracting the random points by taking the illumination area layer as a mask;
step S52: and (3) recording the reflectivity of the corrected red light wave band corresponding to the random sample point in the illumination area to an attribute table of the point by adopting a multi-value extraction to point tool in ArcGIS, making a sample set by combining the attribute table of the random sample point in the illumination area, and dividing the sample set into a training sample set and a testing sample set according to a preset proportion.
Step S53: calling a Random Forest Regressor module integrated in python, taking SEVI of the training sample set as an independent variable, and taking the red light wave band surface reflectivity after SCS + C correction of the training sample set as a dependent variable to construct an RF regression model;
step S54: and calling a Bayesian Optimization module integrated in python to adjust the hyper-parameters in the RF regression model so as to optimize the RF regression model.
Step S55: taking SEVI of the terrain shadow area as an independent variable, and adopting an RF regression model after parameter adjustment and optimization to predict and obtain the reflectivity of the red light wave band of the terrain shadow area;
in this embodiment, step S7 specifically includes the following steps:
step S71: as can be seen from the visual analysis of FIG. 2, the topographic influence is significant before the near infrared equal-band correction is not carried out, and the topographic shadow is spread; the near infrared band corrected by the method of the invention is combined with a 'landform falling shadow correction method of visible band landform of remote sensing image (patent number 2021101469628)' the landform shadow on the near red-green false color synthetic image generated by the corrected red and green bands disappears, the landform influence is eliminated, and the image is distributed in a plane.
Step S72: the near infrared band reflectivity and cosi scatter diagram analysis are adopted to show that: before terrain correction, the correlation is strong, r20.70, RMSE 0.30; after SCS + C correction, the correlation decreases, r2When the value is 0.01 and the value is RMSE is 0.38, the SCS + C correction is effective, but the samples of the falling shadow are mainly located below the regression line, which indicates that the falling shadow correction effect is not good; after the method of the invention is corrected, r2The RMSE is 0.01 and 0.36, and the sunny slope, the umbra and the falling shadow samples are symmetrically distributed on the upper side and the lower side of the regression line, which shows that the integral correction effect is better.
Step S73: rose diagram analysis shows that the numerical values of the instinct shadow and the falling shadow of the near-infrared band without terrain correction are small and are positioned in the inner core of the diagram; the sunny slope is large in value and is distributed to be half-shell outside. After SCS + C correction, the numerical values of the reflection home shadow and the falling shadow of the near-infrared band become larger, and a radial shape from the inner core to the outside is formed; the distribution of the positive slope values forms a half-shell shape, indicating that the SCS + C correction has an effect on the terrain shading correction. After the method is corrected, numerical values of the principal shadow, the falling shadow and the sunny slope of the reflectivity of the near-infrared band are close to form a closed circle and are uniformly distributed in the slope direction of 0-360 degrees, and the method shows that the method has a remarkable effect of correcting the terrain shadow of the near-infrared band.
Step S74: the method adopts the near-infrared band reflectivity error of the relative sunny slope of the shadow and the falling shadow to evaluate the near-infrared band terrain shadow correction effect, and the formula is as follows:
the correction result (fig. 4) shows that the relative errors of the principal and falling shadows and the sunny slope of the surface reflectivity of the near infrared band without terrain correction are large and are 84.01% and 86.34%, respectively. After SCS + C correction, the relative errors of the principal shadow, the falling shadow and the sun slope of the near-infrared band reflectivity are respectively reduced to 16.38 percent and 43.26 percent, which shows that the SCS + C correction has good correction effect on the principal shadow and poor correction effect on the falling shadow. After the method is corrected, the relative errors of the origin shadow, the falling shadow and the sunny slope of the reflectivity of the near-infrared band are respectively reduced to 0.90 percent and 0.83 percent, which shows that the method has a remarkable effect of correcting the terrain shadow of the near-infrared band.
Claims (6)
1. A method for correcting the terrain shadow of a remote sensing image near-infrared wave band used for monitoring mountainous regions is characterized by comprising the following steps:
step S1: acquiring satellite remote sensing data and Digital Elevation Model (DEM) data of a research area, and performing data preprocessing;
step S2: extracting a terrain shadow by adopting machine learning and water body index (NDWI);
step S3: SCS + C terrain correction is carried out by adopting the earth surface reflectivity after atmospheric correction;
step S4: calculating a terrain Shadow Elimination Vegetation Index (SEVI) by adopting the surface reflectivity;
step S5: carrying out terrain shadow correction on red light wave bands;
step S6: calculating the shadow near-infrared band reflectivity by using the shadow red band reflectivity according to the following formula:
ρnir t regret＝RVI·ρr t regret＝SEVI·ρr t regret, (1)
In the formula, ρnir-tcCorrecting the reflectivity, rho, of the near infrared band in the shadow region for terrainr-tcThe reflectivity of the red wave band in the shadow area after the terrain correction is carried out, and RVI is a specific vegetation index;
step S7: and (3) evaluating the correction effect, namely evaluating the terrain correction effect by adopting visual analysis, spectral statistical analysis, relative error analysis and the like, wherein the spectral statistical analysis specifically comprises reflectivity and cosi scattered point analysis and a rose diagram.
2. The method for correcting the terrain shadow of the near-infrared band of the remote sensing image for monitoring mountainous regions as claimed in claim 1, wherein the step S1 is specifically as follows:
step S11, collecting satellite remote sensing data and Digital Elevation Model (DEM) data of a research area;
step S12, carrying out radiometric calibration and atmospheric correction on the satellite remote sensing data to obtain the earth surface reflectivity data of the research area;
and step S13, performing projection transformation, resampling and cutting on the digital elevation model DEM data to obtain the digital elevation model DEM data of the research area, and finally standardizing the obtained data into the same data format.
3. The method for correcting the terrain shadow of the remote sensing image in the near-infrared waveband used for monitoring the mountainous region as claimed in claim 1, wherein the step S2 is specifically as follows:
step S21: respectively selecting three types of sample points, namely a shadow area, an illumination area and a water body, in a research area;
step S22: and classifying the research area by combining a random forest, namely an RF classification method, with the three types of sample points, extracting the overall shadow, and further separating the interference of the water body by using the water body index NDWI.
4. The method for correcting the terrain shadow of the near-infrared band of the remote sensing image for monitoring mountainous regions as claimed in claim 1, wherein the step S3 is specifically as follows:
step S31: and calculating a cosine value cosi of the sun incident angle by combining the DEM data of the digital elevation model of the research area and the solar elevation angle and solar azimuth angle data of the remote sensing image, wherein the calculation formula is as follows:
cosi＝cosσcosθ+sinσsinθcos(β-ω) (2)
wherein i represents a solar incident angle; sigma represents a terrain slope angle; theta represents the solar zenith angle; beta represents a terrain slope angle; ω represents the sun azimuth;
step S32: and calculating the correction parameter c by combining cosi, wherein the calculation formula is as follows:
LT＝a+bXcosi (3)
c＝a/b (4)
in the formula, LTIs the surface reflectance value, a and b are the coefficients of the fitted linear equation;
step S33: SCS + C correction is carried out on the earth surface reflectivity data of the red light wave band, and the calculation formula is as follows:
in the formula, LSCS+CRepresenting the pixel values after SCS + C correction; l isTRepresenting the pixel values prior to SCS + C correction.
5. The method for correcting the terrain shadow of the near-infrared band of the remote sensing image for monitoring mountainous regions as claimed in claim 1, wherein the step S4 is specifically as follows:
calculating the SEVI according to the surface reflectivity of the red light wave band and the near infrared wave band, wherein the formula is as follows:
in the formula, ρrFor the reflectance of the red band of the remote-sensing image, ρnirF (delta) is the reflectivity of the near infrared band of the remote sensing image and is an adjusting factor;
wherein, f (delta) is calculated by the following steps: calculating a gradient map by using a Digital Elevation Model (DEM), resampling the gradient map into blocks with 6 multiplied by 6 square kilometers of spatial resolution, and selecting the first 1% of blocks with the maximum gradient as a steep block set; calculating SEVI and the information entropy H thereof block by adopting the remote sensing image reflectivity data according to the space range of each steep block, wherein the formula is as follows:
in the formula, piThe ratio of the value of i pixel SEVI to the total value of SEVI of the steep slope blocks is taken as n is the number of pixels of the steep slope blocks, SEVIiTaking the SEVI value of the i pixel;
starting the adjustment factor f (delta) from 0, sequentially increasing by a preset step length of 0.001, calculating the SEVI information entropy of the steep block, and taking the adjustment factor f (delta) corresponding to the maximum H as the optimization solution f of the adjustment factor of the steep block when the value of H is maximumbThe formula is as follows:
fb＝argmax(H)，f(Δ)∈(0.001，1.000) (9)
comparing the information entropies of different steep slope blocks, and selecting the adjustment factor corresponding to the pure vegetation steep slope block with the largest information entropy as the optimal adjustment factor f of the whole scene imageoptThe formula is as follows:
fopt＝argmax(H)，fb∈(fbj，j＝1，...，m) (10)
wherein m is the number of the steep blocks.
6. The method for correcting the terrain shadow of the near-infrared band of the remote sensing image for monitoring mountainous regions as claimed in claim 1, wherein the step S5 is specifically as follows:
step S51: taking the whole research area as a processing range, generating a certain number of random points by adopting an ArcGIS random point generating tool, and extracting the random points by taking the illumination area layer as a mask;
step S52: recording the reflectivity of the corrected red light wave band corresponding to the random sample point of the illumination area in an attribute table of the point by adopting a multi-value extraction to-point tool in ArcGIS, making a sample set by combining the attribute table of the random sample point of the illumination area, and dividing the sample set into a training sample set and a testing sample set according to a preset proportion;
step S53: constructing an RF regression model by taking SEVI of the training sample set as an independent variable and taking the red light wave band earth surface reflectivity corrected by SCS + C of the training sample set as a dependent variable;
step S54: adjusting the hyper-parameters to optimize the RF regression model;
step S55: and (3) predicting the reflectivity of the red light wave band in the terrain shadow area by using the SEVI in the terrain shadow area as an independent variable and adopting an RF regression model after parameter adjustment and optimization.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115359369A (en) * | 2022-10-19 | 2022-11-18 | 中国科学院、水利部成都山地灾害与环境研究所 | Mountain satellite image fusion method and system based on time phase self-adaption |
| CN115546648A (en) * | 2022-10-24 | 2022-12-30 | 南京师范大学 | DEM (digital elevation model) alluvial fan extraction method based on ray tracing |
| CN116862798A (en) * | 2023-07-07 | 2023-10-10 | 昆明理工大学 | Topographic shadow effect correction method for optical satellite remote sensing image |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115359369A (en) * | 2022-10-19 | 2022-11-18 | 中国科学院、水利部成都山地灾害与环境研究所 | Mountain satellite image fusion method and system based on time phase self-adaption |
| CN115359369B (en) * | 2022-10-19 | 2023-01-24 | 中国科学院、水利部成都山地灾害与环境研究所 | Mountain satellite image fusion method and system based on time phase self-adaption |
| CN115546648A (en) * | 2022-10-24 | 2022-12-30 | 南京师范大学 | DEM (digital elevation model) alluvial fan extraction method based on ray tracing |
| CN116862798A (en) * | 2023-07-07 | 2023-10-10 | 昆明理工大学 | Topographic shadow effect correction method for optical satellite remote sensing image |
| CN116862798B (en) * | 2023-07-07 | 2024-01-12 | 昆明理工大学 | Topographic shadow effect correction method for optical satellite remote sensing image |
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