Multi-source satellite remote sensing image rapid change detection method

Abstract

The invention provides a multi-source satellite remote sensing image rapid change detection method, which simultaneously realizes relative radiation correction and change area pre-detection of a multi-source sensor through a pixel-oriented projection transformation method, performs preliminary screening on areas which may have changes, only processes the areas which may have changes, and improves the detection accuracy of the multi-source satellite remote sensing image rapid change. Multi-temporal image classification segmentation global operation is avoided, and the image segmentation calculation amount can be greatly reduced; and four types of ground features including buildings, water bodies, vegetation and bare land are identified and segmented for a local area where changes possibly exist, so that the problem that a pixel-level change detection method is influenced by illumination and noise is avoided, and the problem that change detection is inaccurate due to object segmentation errors caused by too small target range on a global image is also avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing image rapid processing and computer vision, and in particular relates to a remote sensing image rapid change detection method. Background Art

[0002] Change detection is the process of discovering changes on the earth's surface by comparing two or more remote sensing images acquired at different time points in the same geographical area. It has been widely used in the fields of land use type change analysis, disaster assessment, resource and environmental monitoring, etc. Traditional change detection methods are sensitive to the radiometric differences and noise of multi-temporal remote sensing images. Due to the differences in shooting angles and shooting times, there are geometric errors and radiometric differences between remote sensing images taken by different sensors at different times, which affects the accuracy of subsequent change detection.

[0003] Existing change detection methods are mainly divided into pixel-oriented and object-oriented detection methods. Pixel-oriented change detection methods are simple and fast to calculate, but the results are fragmented and prone to pseudo-change information similar to noise; on the other hand, object-oriented detection methods require classification and segmentation of two remote sensing images separately, which requires large amounts of data to be processed and their accuracy is affected by the segmentation algorithm. Summary of the invention

[0004] The purpose of the present invention is to provide a method for rapid change detection of multi-source satellite remote sensing images. The relative radiation correction of multi-source sensors and pre-detection of change areas are performed on multi-temporal images through IR-MAD transformation. The trained YOLO8-seg segmentation network is used to segment objects in areas where changes may occur. Finally, a differential calculation is performed to obtain the change detection result. This method can reduce the amount of calculation for classification and segmentation of multi-temporal images while improving its accuracy and efficiency.

[0005] The present invention is implemented by the following technical solutions:

[0006] A method for detecting rapid changes in satellite remote sensing images comprises the following steps:

[0007] Step 1, geometric registration: take the first moment image Img1 as the reference image, use the feature extraction algorithm to extract geometric features of the first moment image Img1 and the second moment image Img2 respectively, and perform feature matching; obtain the geometric mapping relationship between the second moment image Img2 and the first moment image Img1 by establishing an affine transformation model, and perform a geometric transformation on the second moment image Img2 according to the geometric mapping relationship to obtain the registration image IMg'2 of the second moment compared with the first moment, so that the two images are in the same spatial coordinate system;

[0008] Step 2, IR-MAD transformation: Perform iterative weighted multivariate detection algorithm IR-MAD transformation on the first moment image Img1 and the registered image Img'2 obtained in step 1 to find out the unchanged pixel sample PIF and the changed pixel sample CIF. The unchanged pixel sample PIF and the changed pixel sample CIF respectively record the position information of the unchanged pixel in the image and the position information of the changed pixel in the image;

[0009] Step 3: Relative radiation correction: Based on the unchanged pixel sample set PIF obtained in step 2, a relative radiation correction model is constructed, and the relative radiation correction model is solved to obtain radiation correction model parameters. The relative radiation correction model parameters are used to perform radiation correction on the registration image Img'2 obtained in step 1 to obtain the corrected image Img at the second moment. 2_new ;

[0010] Step 4: Target segmentation: For the image Img1 at the first moment and the corrected image Img at the second moment obtained in step 3 2_new , set the slice window size Size, slice into blocks, and obtain the slice sequence Tile1 of the first moment image Img1 and the corrected image Img at the second moment 2_new Slice sequence Tile2; traverse the slices, when the first moment image Img1 and the second moment corrected image Img 2_new When the slices C1 and C2 at the same position contain the changed pixel samples CIF obtained in step 2, the slices C1 and C2 are segmented respectively, otherwise it is skipped; the slices C1 and C2 are segmented using the trained target segmentation network model, and the target type class1 and target shape vector shape1 in the slice C1, and the target type class2 and target shape vector shape2 in the slice C2 are extracted, and stored in the segmentation results Tar1 and Tar2 respectively;

[0011] Step 5: Differential detection: Perform differential calculation on the target shape vectors shape1 and shape2 in the segmentation results Tar1 and Tar2 obtained in step 4, and perform threshold segmentation to obtain the changed target shape vector shape change , the target type class1 corresponding to Tar1 and the class2 corresponding to Tar2 are respectively used as the target type class before and after the change before ,class after , store the change result C = [shape change ,class before ,class after ].

[0012] Furthermore, step one specifically includes: taking the first image Img1 as the reference image, using the feature extraction algorithm SIFT operator to extract geometric features of the first image Img1 and the second moment image Img2 respectively, and generating a 128-dimensional SIFT feature descriptor; setting the neighborhood radius to calculate the distance between each pair of key points of the SIFT feature descriptor, and returning the point corresponding to the minimum distance as the best matching point; according to the coordinates (x1, y1) of the best matching point on the first moment image Img1 and the coordinates (x2, y2) of the second moment image Img2, substitute them into the affine transformation model to obtain the affine transformation parameters; according to the affine transformation parameters, perform a geometric transformation on the second moment image Img2 to obtain the registered image Img'2 of the second moment compared with the first moment, so that the two images are in the same spatial coordinate system.

[0013] Furthermore, step 2 specifically includes: performing linear transformations U and V respectively according to the spectral vector values ​​of the two images Img1 and Img'2, and calculating the MAD variable according to the difference between the two transformation vectors; calculating the invariant probability P of each pixel according to the MAD no_change As the iteration weight, P no_change Repeat the iteration as the next iteration weight until the two typical correlation coefficients ρ N-i+1 The difference is less than the threshold λ or the number of iterations reaches the threshold T. no_change The set of pixel positions greater than the threshold p is taken as PIF, and the remaining pixels are taken as changed pixel samples CIF.

[0014] Furthermore, step three specifically includes: according to the unchanged pixel sample set PIF obtained in step two, obtaining the pixel reflectance set I1 of the first moment image Img1 corresponding to PIF and the pixel reflectance set I2 of the registration image Img'2 obtained in step one, constructing a relative radiation correction model according to I1 and I2, and using the least squares method to calculate the radiation correction model parameters; using the least squares method to solve the linear regression parameters; according to the solved linear regression parameters, performing radiation correction on the registration image Img'2 obtained in step one to obtain the corrected image Img'2 at the second moment 2_new .

[0015] Furthermore, in step 4, the slice window size Size is 512 or 1024.

[0016] Furthermore, the target segmentation network model trained in step 4 is a YOLOv8m-seg network model.

[0017] Furthermore, the target shape vector in step 4 is used to record the XY set of vertices of each target polygon.

[0018] Furthermore, the target types in step 4 and step 5 include buildings, water bodies, vegetation, and bare land.

[0019] The present invention realizes relative radiation correction of multi-source sensors and pre-detection of changed areas through a pixel-oriented projection transformation method, performs preliminary screening of areas where changes may occur, and only processes areas where changes may occur, avoiding global operations of multi-temporal image classification and segmentation, which can greatly reduce the amount of image segmentation calculations; then, four types of objects, namely buildings, water bodies, vegetation, and bare land, are identified and segmented for local areas where changes may occur. On the one hand, the problem that the pixel-level change detection method is affected by illumination and noise is avoided, and at the same time, the problem that the target range is too small on the global image, resulting in object segmentation errors and inaccurate change detection, is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a method for detecting rapid changes in remote sensing images in one embodiment of the present invention;

[0021] Figure 2 It is a schematic diagram of the detection results of one embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 The embodiment of the present invention provides a remote sensing image rapid change detection method, comprising the following steps:

[0024] Step 1: Geometric registration. Taking the first moment image Img1 as the reference image, use the feature extraction algorithm to extract geometric features from the first moment image Img1 and the second moment image Img2 respectively, and perform feature matching; obtain the geometric mapping relationship between the second moment image Img2 and the first moment image Img1 by establishing an affine transformation model, and perform a geometric transformation on the second moment image Img2 according to the relationship to obtain the registration image Img'2 of the second moment compared to the first moment Img1, so that the two images are in the same spatial coordinate system.

[0025] The step 1 specifically includes:

[0026] Taking the first image Img1 as the reference image, the feature extraction algorithm SIFT operator is used to extract geometric features from the first image Img1 and the second moment image Img2 respectively, and a 128-dimensional SIFT feature descriptor is generated; the neighborhood radius is set to calculate the distance between each pair of key points of the SIFT feature descriptor, and the point corresponding to the minimum distance is returned as the best matching point. According to the coordinates (x1, y1) of the best matching point on the first moment image Img1 and the coordinates (x2, y2) of the second moment image Img2, the affine transformation model is introduced to obtain the affine transformation parameters; according to the affine transformation parameters, the second moment image Img2 is geometrically transformed to obtain the registration image Img'2 of the second moment compared with the first moment Img1, so that the two images are in the same spatial coordinate system.

[0027] Step 2: IR-MAD transformation: Perform an iterative weighted multivariate detection algorithm IR-MAD transformation on the first moment image Img1 and the registered image Img'2 obtained in step 1 to find the unchanged pixel sample PIF and the changed pixel sample CIF. The unchanged pixel sample PIF and the changed pixel sample CIF respectively record the position information of the unchanged pixel in the image and the position information of the changed pixel in the image.

[0028] The step 2 specifically includes:

[0029] According to the spectral vector values ​​of the two images Img1 and Img'2, linear transformations U and V are performed respectively, and the MAD variable is calculated based on the difference between the two transformation vectors; the invariant probability P of each pixel is calculated based on the MAD no_change As the iteration weight, P no_change Repeat the iteration as the next iteration weight until the two typical correlation coefficients ρ N-i+1 The difference is less than the threshold λ or the number of iterations reaches the threshold T. no_change The set of pixel positions greater than the threshold p is taken as PIF, and the remaining pixels are taken as changed pixel samples CIF.

[0030] Step 3: Relative radiation correction: Based on the unchanged pixel sample set PIF obtained in step 2, a relative radiation correction model is constructed, and the relative radiation correction model is solved to obtain radiation correction model parameters. The relative radiation correction model parameters are used to perform radiation correction on the registration image Img'2 obtained in step 1 to obtain the corrected image Img at the second moment. 2_new ;

[0031] The step three specifically includes:

[0032] According to the unchanged pixel sample set PIF obtained in step 2, the pixel reflectance set I1 of the first moment image Img1 corresponding to PIF and the pixel reflectance set I2 of the registered image Img'2 obtained in step 1 are obtained. A relative radiation correction model is constructed based on I1 and I2, and the radiation correction model parameters are calculated using the least squares method. The linear regression parameters are solved using the least squares method; according to the solved linear regression parameters, the registered image Img'2 obtained in step 1 is subjected to radiation correction to obtain the corrected image Img at the second moment. 2_new ;

[0033] Step 4: Target segmentation. For the image Img1 at the first moment and the corrected image Img at the second moment obtained in step 3 2_new , manually set the slice window size Size (Size is usually 512 or 1024), and obtain the slice sequence Tile1 of the first moment image Img1 and the corrected image Img at the second moment 2_new Slice sequence Tile2; traverse all slices, when Img1 and Img 2_new When the slices C1 and C2 at the same position contain the changed pixel sample CIF obtained in step 2, the slice is segmented, otherwise the slice is skipped. Based on the pre-trained YOLOv8m-seg network model target segmentation network, the pixel sets C1 and C2 are segmented respectively, and the target type class1 (target types include buildings, water bodies, vegetation, and bare land) and target shape vector shape1 (recording the XY set of each target polygon vertex) in C1, and the target type class2 and target shape vector shape2 (recording the XY set of each target polygon vertex) in C2 are extracted, and stored in the target sets Tar1 and Tar2 respectively, that is, [class1, shape1]∈Tar1, [class2, shape2]∈Tar2;

[0034] Step 5: Differential detection: Perform differential calculation on the target shape vector shape2 and shape1 in the segmentation results Tar1 and Tar2 obtained in step 4, and perform threshold segmentation to obtain the changed target shape vector shape change , the target type class1 corresponding to Tar1 and the class2 corresponding to Tar2 are respectively used as the target type class before and after the change before ,class after , store the change result c = [shape change ,class before ,class after ].

[0035] The following is a specific example to illustrate:

[0036] Step 1: Geometric registration: Perform geometric registration on the two images Img1 and Img2 at different times to obtain the registered images Img1 and Img'2.

[0037] (1) Taking the first moment image Img1 as the reference image, extract the SIFT features of Img1 and Img2, and generate 128-dimensional SIFT feature descriptors S1 and S2.

[0038] (2) Feature matching. Set the neighborhood radius R, calculate the distance between the 128-dimensional SIFT feature descriptors S1 and S2 between each pair of key points in R, and return the minimum distance among the k best matches for each key point (k = 5 in this example). The point with the minimum distance is the matching point of the key point.

[0039] (3) Geometric transformation: Based on the feature matching results, the least squares method is used to solve the affine transformation parameters.

[0040] Assume that the matching point of feature point Y on Img1 on Img2 is X, then:

[0041] x1=ax2+cy2+m (1)

[0042] y1=bx2+dy2+l (2)

[0043] Right now

[0044]

[0045] Among them, a, b, c, d, m, l are affine transformation parameters.

[0046] make: Then Y = AX, find the affine transformation parameter A, then

[0047] A=(X T X) -1 X T Y (4)

[0048] (4) Use formula 3 to perform affine transformation on Img2 to obtain Img'2.

[0049] Step 2: IR-MAD transformation: Perform an iterative weighted multivariate detection algorithm IR-MAD transformation on the first moment image Img1 and the registered image Img'2 obtained in step 1 to find the unchanged pixel sample PIF and the changed pixel sample CIF. The unchanged pixel sample PIF and the changed pixel sample CIF respectively record the position information of the unchanged pixel in the image and the position information of the changed pixel in the image.

[0050] (1) Set the initial weight of all pixels to 1,

[0051] (2) Let variable X = [X1…X N ] T ,Y=[Y1…Y N ] T They correspond to the spectral vector values ​​of the two images Img1 and Img'2, and N is the number of bands. Linear transformation is performed on X and Y respectively:

[0052] U=a T X=a1X1+…a i X i +a N X N

[0053] V=b T X=b1Y1+…b i Y i +b N Y N

[0054] Let the MAD variable be the difference between U and V, and obtain the linear transformation parameters of the maximum correlation coefficient between vectors U and V through CCA:

[0055] MAD i =U N-i+1 -V N-i+1 , i=1…N

[0056] make For MAD i The standard deviation of is the variance, ρ N-i+1 is the typical correlation coefficient, then

[0057]

[0058] but:

[0059]

[0060] N MAD is a normalized MAD variable, and the MAD mean for unchanged pixels is approximately 0.

[0061] (3) Calculate the unchanged probability of each pixel through the chi-square test:

[0062] P no_change =P{ 2 (N)>N MAC}

[0063] (4) P no_change As the weight for the next iteration;

[0064] (5) Repeat steps (2) to (4) until ρ N-i+1The difference is less than the threshold λ or the number of iterations reaches the threshold T.

[0065] (6) P no_change The set of pixel positions greater than the threshold p is taken as PIF, and the remaining pixels are taken as changed pixel samples CIF.

[0066] Step 3: Relative radiation correction. Based on the unchanged pixel sample set PIF obtained in step 2, a relative radiation correction model is constructed, and the relative radiation correction model is solved to obtain radiation correction model parameters. The relative radiation correction model parameters are used to perform radiation correction on the registration image Img'2 obtained in step 1 to obtain the corrected image Img at the second moment. 2_new .

[0067] (1) Use the least squares method to find the linear regression parameters of the radiation correction model. Let the spectral values ​​of Img1 and Img'2 pixels at the corresponding positions in PIF be PX i ,PY i ,but:

[0068] PX=A·PY+B

[0069] A and B are linear regression parameters. Least squares method is used to solve the parameters A and B.

[0070] (2) According to the solved A and B values, perform projection transformation on Img'2:

[0071] Img 2_new =A·Img′2+B

[0072] Step 4: Target segmentation. For Img1 and Img 2_new ∈CIF, perform target segmentation based on the target segmentation network, extract the target type in the window W, and obtain Tar1 and Tar2.

[0073] (1) Manually set the slice window size Size = 512, and compare the first moment image Img1 and the second moment corrected image Img obtained in step 3 2_new The whole image is sliced ​​into blocks of size Size to obtain the slice sequence Tile1 of the first moment image Img1 and the corrected image Img at the second moment 2_new Slice sequence Tile2;

[0074] (2) When the image Img1 at the first moment and the image Img after correction at the second moment 2_newWhen the slices C1 and C2 at the same position contain the changed pixel samples CIF obtained in step 2, target segmentation is performed on C1 and C2 respectively, otherwise it is skipped. Use the trained target segmentation network model to segment C1 and C2, extract the target type class1 (target types include buildings, water bodies, vegetation, bare land) and target shape vector shape1 (record each target polygon vertex XY set) in C1, and the target type class2 and target shape vector shape2 in C2, and store them in the segmentation results Tar1 and Tar2 respectively.

[0075] Step 5: Differential detection: Perform differential calculation on Tar1 and Tar2 obtained in step 4 to obtain the change result C.

[0076] (1) For the position (x, y) ∈ CIF, obtain the segmentation results of Tar1 and Tar2, which are [class1, shape1] and [class2, shape2] respectively, and determine whether it is a change

[0077]

[0078] (2) Mark the changed Tar2 and obtain the change-unchange binary classification result. change , the target type class1 and class2 of the target are respectively used as the target type class before and after the change before ,class after , store the change result c = [shape change ,class before ,class after ].

[0079] Figure 2 This is the detection result of one of the embodiments of the present invention. The satellite images of a reservoir area in 2017 and 2022 are registered, IR-MAD transformed, and relatively radiometrically corrected to find the candidate area CIF of change. The 2017 and 2022 images after registration and relative radiometric correction are divided into blocks, and the blocks containing the CIF of the changed pixel samples are traversed. Figure 2 The blue window shown in (a) is regarded as a block with no changed pixel sample CIF and is directly skipped; the red window is regarded as one of the blocks overlapping with the changed pixel sample CIF, and the target recognition segmentation is performed on it. The segmentation results are shown in Figure 2 (b) Figure 2 The segmentation result of (b) is differentially calculated to obtain the change detection result, such as Figure 2 (c) as shown.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting rapid changes in multi-source satellite remote sensing images, characterized in that: The steps include: Step 1, geometric registration: take the first moment image Img1 as the reference image, use the feature extraction algorithm to extract the geometric features of the first moment image Img1 and the second moment image Img2 respectively, and perform feature matching; obtain the geometric mapping relationship between the second moment image Img2 and the first moment image Img1 by establishing an affine transformation model, and perform a geometric transformation on the second moment image Img2 according to the geometric mapping relationship to obtain the registration image Img'2 of the second moment compared with the first moment, so that the two images are in the same spatial coordinate system; Step 2, IR-MAD transformation: Perform iterative weighted multivariate detection algorithm IR-MAD transformation on the first moment image Img1 and the registered image Img'2 obtained in step 1 to find out the unchanged pixel sample PIF and the changed pixel sample CIF. The unchanged pixel sample PIF and the changed pixel sample CIF respectively record the position information of the unchanged pixel in the image and the position information of the changed pixel in the image; Step 3: Relative radiation correction: Based on the unchanged pixel sample set PIF obtained in step 2, a relative radiation correction model is constructed, and the relative radiation correction model is solved to obtain radiation correction model parameters. The relative radiation correction model parameters are used to perform radiation correction on the registration image Img'2 obtained in step 1 to obtain the corrected image Img at the second moment. 2_new ; Step 4: Target segmentation: For the image Img1 at the first moment and the corrected image Img at the second moment obtained in step 3 2_new , set the slice window size Size, slice into blocks, and obtain the slice sequence Tile1 of the first moment image Img1 and the corrected image Img at the second moment 2_new Slice sequence Tile2; traverse the slices, when the image Img1 at the first moment and the corrected image Img at the second moment 2_new When the slices C1 and C2 at the same position contain the changed pixel sample CIF obtained in step 2, the slices C1 and C2 are segmented respectively, otherwise it is skipped; the slices C1 and C2 are segmented using the trained target segmentation network model, and the target type class1 and target shape vector shape1 in the slice C1, and the target type class2 and target shape vector shape2 in the slice C2 are extracted, and stored in the segmentation results Tar1 and Tar2 respectively; Step 5: Differential detection: Perform differential calculation on the target shape vectors shape1 and shape2 in the segmentation results Tar1 and Tar2 obtained in step 4, and perform threshold segmentation to obtain the changed target shape vector shape change , the target type class1 corresponding to Tar1 and the class2 corresponding to Tar2 are respectively used as the target type class before and after the change before ,class after , store the change result C = [shape change ,class before ,class after ].

2. The method for detecting rapid changes in multi-source satellite remote sensing images according to claim 1, characterized in that: Step one specifically includes: taking the first image Img1 as the reference image, using the feature extraction algorithm SIFT operator to extract geometric features from the first image Img1 and the second moment image Img2 respectively, and generating a 128-dimensional SIFT feature descriptor; setting the neighborhood radius to calculate the distance between each pair of key points of the SIFT feature descriptor, and returning the point corresponding to the minimum distance as the best matching point; according to the coordinates (x1, y1) of the best matching point on the first moment image Img1 and the coordinates (x2, y2) of the second moment image Img2, substitute them into the affine transformation model to obtain the affine transformation parameters; according to the affine transformation parameters, perform a geometric transformation on the second moment image Img2 to obtain the registration image IMg'2 of the second moment compared to the first moment, so that the two images are in the same spatial coordinate system.

3. The method for detecting rapid changes in multi-source satellite remote sensing images according to claim 1, characterized in that: Step 2 specifically includes: according to the spectral vector values ​​of the two images Img1 and Img'2, linear transformations U and V are performed respectively, and the MAD variable is calculated according to the difference between the two transformation vectors; according to the MAD, the invariant probability P of each pixel is calculated. no_change As the iteration weight, P no_change Repeat the iteration as the next iteration weight until the two typical correlation coefficients ρ N-i+1 The difference is less than the threshold λ or the number of iterations reaches the threshold T. no_change The set of pixel positions greater than the threshold p is taken as PIF, and the remaining pixels are taken as changed pixel samples CIF.

4. The method for detecting rapid changes in multi-source satellite remote sensing images according to claim 1, characterized in that: According to the unchanged pixel sample set PIF obtained in step 2, the pixel reflectance set I1 of the first moment image Img1 corresponding to PIF and the pixel reflectance set I2 of the registered image Img'2 obtained in step 1 are obtained. A relative radiation correction model is constructed based on I1 and I2, and the radiation correction model parameters are calculated using the least squares method. The linear regression parameters are solved using the least squares method. According to the solved linear regression parameters, the registered image Img'2 obtained in step 1 is subjected to radiation correction to obtain the corrected image Img at the second moment. 2_new .

5. The method for detecting rapid changes in multi-source satellite remote sensing images according to claim 1, characterized in that: In step 4, the slice window size is 512 or 1024.

6. The method for detecting rapid changes in multi-source satellite remote sensing images according to claim 1, characterized in that: The target segmentation network model trained in step 4 is the YOLOv8m-seg network model.

7. The method for detecting rapid changes in multi-source satellite remote sensing images according to claim 1, characterized in that: The target shape vector in step 4 is used to record the XY set of vertices of each target polygon.

8. The method for detecting rapid changes in multi-source satellite remote sensing images according to claim 1, characterized in that: Target types in steps 4 and 5 include buildings, water bodies, vegetation, and bare land.

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