A transformation detection method for remote sensing images based on multi-scale global and local feature fusion

Abstract

The present invention discloses a transformation detection method for remote sensing images based on multi-scale global and local feature fusion. The present invention adaptively mixes a CNN module with a Transformer module, utilizing the advantages of the CNN module in extracting local feature information of the image and the advantages of the multi-scale Transformer module in extracting global feature information of the image, thereby better fusing all global and local information of the high-resolution remote sensing image in the feature extraction stage, ensuring the richness of feature information in the feature extraction layer. At the same time, a multi-scale attention mechanism is used to extract and fuse features at different scales, thereby reducing the complexity of attention calculation and enabling the retention of pixel detail information of the high-resolution remote sensing image, ensuring the expression of shallow and deep information of the high-resolution remote sensing image, and being able to accurately identify changes in high-resolution remote sensing images of the same area at different times. The detection accuracy is high, the computational complexity is low, and the detection efficiency is fast.

Description

Technical Field

[0001] The present invention specifically relates to a transformation detection method for remote sensing images based on multi-scale global and local feature fusion. Background Art

[0002] With the rapid development of remote sensing technology, remote sensing images have been widely used in fields such as environmental monitoring, urban planning, and disaster assessment. Remote sensing image transformation detection technology aims to detect surface changes by comparing and analyzing remote sensing images from different periods. This is of great significance for understanding natural disasters, monitoring environmental changes, and conducting resource management.

[0003] However, current remote sensing image transformation detection methods mainly rely on statistical methods and feature-based detection methods. These methods are computationally complex and have low detection efficiency when facing high-resolution remote sensing images. They are also prone to loss of pixel detail information. The feature information of the feature extraction layer is insufficient, and they cannot accurately identify the changes in remote sensing images of the same area at different times. The detection accuracy is poor and urgently needs to be improved.

[0004] Therefore, it is necessary to invent a transformation detection method for remote sensing images based on multi-scale global and local feature fusion to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a transformation detection method for remote sensing images based on multi-scale global and local feature fusion. By adaptively mixing the CNN module and the Transformer module, all global and local information of the high-resolution remote sensing image are better integrated in the feature extraction stage, thereby ensuring the richness of the feature information of the feature extraction layer. At the same time, a multi-scale attention mechanism is used to extract and fuse features at different scales, thereby reducing the complexity of attention calculation, and enabling the pixel detail information of the high-resolution remote sensing image to be retained, thereby ensuring the expression of shallow and deep information of the high-resolution remote sensing image, thereby being able to accurately identify the changes in the high-resolution remote sensing images of the same area at different times, with high detection accuracy, low computational complexity and fast detection efficiency, so as to solve the above-mentioned shortcomings in the technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a transformation detection method for remote sensing images based on multi-scale global-local feature fusion, comprising the following steps:

[0007] Step 1: Input two high-resolution remote sensing images of the same area at different time periods and group them by channel;

[0008] Step 2: Feature extraction and feature merging are performed on the grouped high-resolution remote sensing images to obtain global and local feature information at multiple scales;

[0009] Step 3: The output of the multi-scale global and local feature information at different scales of the ConvTrans module is input into the CC fusion module for feature fusion and output to obtain f′, f″, f″′, and f″″;

[0010] Step 4: Input f′, f″, f″′, and f″″ into the multi-scale attention decoder of the U-shaped structure to obtain global and local information at multiple scales and generate a predicted change map.

[0011] Step 5: Use the mixed loss function L to predict the change graph Perform parameter optimization.

[0012] In the aforementioned remote sensing image transformation detection method based on multi-scale global-local feature fusion, in step 1, two high-resolution remote sensing images of the same area at different time periods are input and grouped by channel. The specific steps are as follows:

[0013] 1.1, let T 1 Represents the remote sensing image of the previous phase, T 2 represents the post-phase remote sensing image, and T∈{T i ∈R C×H×W |i=1,2};

[0014] Where H is the height of the high-resolution remote sensing image,

[0015] W is the width of the high-resolution remote sensing image,

[0016] C represents the number of channels of the high-resolution remote sensing image, that is, there are C channels;

[0017] 1.2、T i Divide by channel into X 1 ,X 2 The two groups, the grouping results are:

[0018]

[0019] X 1 =ChannelSplit(T i )

[0020] X 2 =ChannelSplit(T i )

[0021] Among them, ChannelSplit() is a channel splitting operation.

[0022] In the aforementioned transformation detection method of remote sensing images based on multi-scale global-local feature fusion, in step 2, feature extraction and feature merging are performed on the grouped high-resolution remote sensing images to obtain global and local feature information at multiple scales, namely:

[0023] T 1 、T 2 X 1 ,X 2 The outputs are input into the hybrid module of Transformer module and CNN module respectively, and the outputs of the two modules are combined to obtain global and local feature information at multiple scales;

[0024] Among them, the global and local feature information at multiple scales includes T 1 The global and local feature information F 11 , global and local feature information F 21 , global and local feature information F 31 , global and local feature information F 41 ;

[0025] and T 2 The global and local feature information F 12 , global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 .

[0026] In the aforementioned transformation detection method of remote sensing images based on multi-scale global and local feature fusion, in step 2, specifically, the global and local feature information F is obtained based on the first ConvTransBlock module. 11 The steps are as follows:

[0027] 2.1、X 1 Input to the CNN module with a convolutional layer size of 3×3 and downsample it, and extract X 1 Local feature information The specific formula is as follows:

[0028]

[0029] Among them, Conv(·) is the convolution operation;

[0030] 2.2, X 2 After downsampling through the 1×1 convolutional layer, the input is fed into the Transformer module to extract X 2 Global feature information The specific formula is as follows:

[0031] F Z =Conv(X2 )

[0032]

[0033] Where MST(·) represents the Multiscale Transformer module;

[0034] 2.3 Local feature information and global feature information Perform channel splicing to obtain global and local feature information F 11 , the specific formula is as follows:

[0035]

[0036] in, Represents a channel splicing operation.

[0037] In the aforementioned transformation detection method of remote sensing images based on multi-scale global and local feature fusion, in step 2, the global and local feature information F is obtained. 21 , global and local feature information F 31 , global and local feature information F 41 The specific steps are as follows:

[0038] 2.4. Taking the second ConvTransBlock module as the benchmark, the global local feature information F 11 Input into the ConvTransBlock module, group by channel, and repeat steps 2.1-2.3 to obtain the global and local feature information F 21 ;

[0039] 2.5, based on the third ConvTransBlock module, the global local feature information F 21 Input into the ConvTransBlock module, group by channel, and repeat steps 2.1-2.3 to obtain the global and local feature information F 31 ;

[0040] 2.6, based on the fourth ConvTransBlock module, the global local feature information F 31 Input into the ConvTransBlock module, group by channel, and repeat steps 2.1-2.3 to obtain the global and local feature information F 41 .

[0041] In the aforementioned transformation detection method of remote sensing images based on multi-scale global and local feature fusion, in step 2, T is obtained. 2 The global and local feature information F 12 , global and local feature information F22 , global and local feature information F 32 , global and local feature information F 42 The specific steps are:

[0042] Repeat steps 2.1-2.6 to obtain the global and local feature information F 12 , global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 ;

[0043] Among them, T 1 、T 2 The outputs of the hybrid network of the Transformer module and CNN module of the two branches at four different stages are

[0044] In the aforementioned transformation detection method for remote sensing images based on multi-scale global-local feature fusion, in step 3, the output of the multi-scale global-local feature information at different scales of the ConvTrans module is input into the CC fusion module for feature fusion and output to obtain f′, f″, f″′, and f″″. The specific steps are as follows:

[0045] The outputs of the ConvTrans module at different scales are input to the CC fusion module for channel splicing, and a 1×1 convolution is performed to obtain f′, f″, f″′, and f″″. The specific formula is as follows;

[0046]

[0047] In the aforementioned remote sensing image transformation detection method based on multi-scale global and local feature fusion, in step 4, f′, f″, f″′, and f″″ are input into the multi-scale attention decoder of the U-shaped structure to obtain global and local information at multiple scales and generate a predicted change map. The specific steps are as follows:

[0048] 4.1. Input f′ into the MSA module and downsample to obtain output D1. Add D1 and f″ to obtain D1′, which is then input into the MSA module for downsampling to obtain D2. Add D2 and f″′ to obtain D2′, which is then input into the MSA module for downsampling to obtain D3. Add D3 and f″″ to obtain D3′, which is then input into the MSA module for downsampling to obtain D4. The specific formula is as follows:

[0049] D1=MSA(f′)

[0050]

[0051] D2=MSA(D1′)

[0052]

[0053] D3=MSA(D2′)

[0054]

[0055] D4=MSA(D3′)

[0056] Among them, MSA(·) represents the multi-scale attention module;

[0057] 4.2. Upsample and merge D1, D2, D3, and D4 and input them into the corresponding ECA module to restore the resolution of the two high-resolution remote sensing images and generate a predicted change map. The specific formula is as follows:

[0058] d4′=ECA(D4)

[0059]

[0060] Among them, d i i={1, 2, 3, 4} in ′,

[0061] Among them, ECA(·) is the operation of the ECA module,

[0062] is the matrix multiplication operation,

[0063] Up(·) is an upsampling operation.

[0064] In the aforementioned remote sensing image transformation detection method based on multi-scale global and local feature fusion, in step 5, the hybrid loss function L is used to predict the change map. Parameter optimization is performed, and the specific details are as follows:

[0065] L=L wce +L dice

[0066]

[0067] Among them, L is the total mixed loss function, that is, the weighted cross entropy loss L wce and Dice loss L dice of and;

[0068] It is the predicted change graph An element in represents the prediction result of a specific position in the graph.

[0069] Y is the true change graph,

[0070] L wce is the weighted cross entropy loss,

[0071] L dice Dice loss.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The present invention adaptively mixes the CNN module and the Transformer module, utilizes the advantage of the CNN module in extracting local feature information of the image, and the advantage of the multi-scale Transformer module in extracting global feature information of the image, thereby better fusing all global and local information of the high-resolution remote sensing image in the feature extraction stage, ensuring the richness of feature information of the feature extraction layer. At the same time, a multi-scale attention mechanism is used in the U-shaped decoder to extract and fuse features at different scales and perform dimensionality reduction operations on high-dimensional features, which not only reduces the complexity of attention calculation, but also effectively solves the limitation that single-scale feature extraction is often difficult to fully capture the information in high-resolution remote sensing images, ensures that the pixel detail information of the high-resolution remote sensing image can be retained, thereby accurately identifying the changes in the high-resolution remote sensing images of the same area at different times, ensuring the expression of shallow and deep information of the high-resolution remote sensing images, improving the accuracy of change detection of high-resolution remote sensing images, and low detection calculation complexity and high detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0075] Figure 1 is a flow chart of the present invention;

[0076] Figure 2 It is the front phase remote sensing image of the present invention;

[0077] Figure 3 is the post-temporal remote sensing image of the present invention;

[0078] Figure 4 It is the target effect diagram of the present invention;

[0079] Figure 5 It is a real label schematic diagram of the present invention. DETAILED DESCRIPTION

[0080] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0081] The present invention provides Figure 1-5 The transformation detection method of remote sensing images based on multi-scale global and local feature fusion shown in the figure includes the following steps:

[0082] Step 1: Input two high-resolution remote sensing images of the same area at different times and group them by channel. The specific steps are as follows:

[0083] 1.1, let T 1 Represents the remote sensing image of the previous phase, T 2 represents the post-phase remote sensing image, and T∈{T i ∈R C×H×W |i=1,2};

[0084] Where H is the height of the high-resolution remote sensing image,

[0085] W is the width of the high-resolution remote sensing image,

[0086] C represents the number of channels of the high-resolution remote sensing image, that is, there are C channels;

[0087] 1.2、T i Divide by channel into X 1 ,X 2 The two groups, the grouping results are:

[0088]

[0089] X 1 =ChannelSplit(T i )

[0090] X 2 =ChannelSplit(T i )

[0091] Among them, ChannelSplit(·) is the channel splitting operation;

[0092] In this step, the computational complexity can be reduced by dividing the high-resolution remote sensing image into channels and processing the X 1 ,X 2 The two sets of channels can reduce the computational burden and facilitate the subsequent CNN convolution operation on one part of the channel and the attention operation of the Transformer module on the other part, respectively extracting features and facilitating better acquisition of global and local feature information at multiple scales.

[0093] Step 2: Perform feature extraction and feature merging on the grouped high-resolution remote sensing images to obtain global and local feature information at multiple scales, namely:

[0094] T 1 、T 2 X 1 ,X 2 The outputs are input into the hybrid module of Transformer module and CNN module respectively, and the outputs of the two modules are combined to obtain global and local feature information at multiple scales;

[0095] Among them, the global and local feature information at multiple scales includes T 1 The global and local feature information F 11 , global and local feature information F 21 , global and local feature information F 31 , global and local feature information F 41 ;

[0096] and T 2 The global and local feature information F 12 , global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 ;

[0097] Specifically, taking the first ConvTransBlock module as the benchmark, we get the global local feature information F 11 The steps are as follows:

[0098] 2.1、X 1 Input to the CNN module with a convolutional layer size of 3×3 and downsample it, and extract X 1 Local feature information The specific formula is as follows:

[0099]

[0100] Among them, Conv(·) is the convolution operation;

[0101] 2.2, X 2 After downsampling through the 1×1 convolutional layer, the input is fed into the Transformer module to extract X 2 Global feature information The specific formula is as follows:

[0102] F Z =Conv(X 2 )

[0103]

[0104] Where MST(·) represents the Multiscale Transformer module;

[0105] 2.3 Local feature information and global feature information Perform channel splicing to obtain global and local feature information F 11 , the specific formula is as follows:

[0106]

[0107] in, Represents channel splicing operation;

[0108] At this time, the global local feature information F is obtained 21 , global and local feature information F 31 , global and local feature information F 41 The specific steps are as follows:

[0109] 2.4. Taking the second ConvTransBlock module as the benchmark, the global local feature information F 11 Input into the ConvTransBlock module, group by channel, and repeat steps 2.1-2.3 to obtain the global and local feature information F 21 ;

[0110] 2.5, based on the third ConvTransBlock module, the global local feature information F 21 Input into the ConvTransBlock module, group by channel, and repeat steps 2.1-2.3 to obtain the global and local feature information F 31 ;

[0111] 2.6, based on the fourth ConvTransBlock module, the global local feature information F 31 Input into the ConvTransBlock module, group by channel, and repeat steps 2.1-2.3 to obtain the global and local feature information F 41 ;

[0112] Among them, we get T 2 The global and local feature information F 12 , global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 The specific steps are:

[0113] Repeat steps 2.1-2.6 to obtain the global and local feature information F 12, global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 ;

[0114] At this time, T 1 、T 2 The outputs of the hybrid network of the Transformer module and CNN module of the two branches at four different stages are

[0115] In this step, the ConvTransBlock module is a hybrid network structure that combines the Transformer module and the CNN module, which can enhance representation learning by fusing local features and global representations;

[0116] At the same time, because after each Block, T 1 、T 2 It is continuously down-sampled, and since there are four blocks in this method, there are four dimensions, corresponding to the output of different blocks, that is, the ConvTransBlock module in the first stage processes T 1 Block 1 obtains the global and local feature information F 11 Lose, process T 2 The output of Block1 is the global local feature information F 12 , and so on, we can obtain global and local feature information at multiple scales;

[0117] In this step, by adopting a module that adaptively mixes the two methods, the feature weights of the CNN and Transformer modules are dynamically adjusted, making the fusion of global feature information and local feature information in the hybrid network more intelligent and efficient at different stages, ensuring the richness of feature information in the feature extraction layer, and better solving the problem of missing global and local information when using only CNN modules or Transformer modules in the traditional way.

[0118] Step 3: The output of the multi-scale global and local feature information at different scales of the ConvTrans module is input into the CC fusion module for feature fusion and output to obtain f′, f″, f″′, and f″″. The specific steps are as follows:

[0119] 3.1. The outputs of the ConvTrans module at different scales are input to the CC fusion module for channel splicing, and a 1×1 convolution is performed to obtain f′, f″, f″′, and f″″. The specific formula is as follows;

[0120]

[0121]

[0122] Among them, the role of the CC fusion module is to effectively integrate features of different scales, sources, and levels, and balance the contributions of different data sources through splicing operations;

[0123] In this step, the CC fusion module is used to effectively merge the feature maps of the same level but different sources, so that the information captured by the model complements each other. The fusion of multi-source data can improve the model's sensitivity and robustness to changing information and enhance the model's ability to detect diverse changes.

[0124] Step 4: Input f′, f″, f″′, and f″″ into the multi-scale attention decoder of the U-shaped structure to obtain global and local information at multiple scales and generate a predicted change map. The specific steps are as follows:

[0125] 4.1. Input f′ into the MSA module and downsample to obtain output D1. Add D1 and f″ to obtain D1′, which is then input into the MSA module for downsampling to obtain D2. Add D2 and f″′ to obtain D2′, which is then input into the MSA module for downsampling to obtain D3. Add D3 and f″″ to obtain D3′, which is then input into the MSA module for downsampling to obtain D4. The specific formula is as follows:

[0126] D1=MSA(f′)

[0127]

[0128] D2=MSA(D1′)

[0129]

[0130] D3=MSA(D2′)

[0131]

[0132] D4=MSA(D3′)

[0133] Among them, MSA(·) represents the multi-scale attention module;

[0134] 4.2. Upsample and merge D1, D2, D3, and D4 and input them into the corresponding ECA module to restore the resolution of the two high-resolution remote sensing images and generate a predicted change map. The specific formula is as follows:

[0135] d4′=ECA(D4)

[0136]

[0137] Among them, d i 'i={1, 2, 3, 4};

[0138] ECA(·) is the operation of the ECA module,

[0139] is the matrix multiplication operation,

[0140] Up(·) is the upsampling operation;

[0141] In this step, by combining the multi-scale attention mechanism with the ECA module, downsampling and upsampling are performed layer by layer to achieve feature decoding and fusion. Among them, the MSA module can capture change information at different scales and enhance global context perception. At the same time, the gradual fusion of features in each layer helps to combine shallow details and deep semantics. The ECA module further strengthens important channel features and improves the model's ability to detect changed areas. The overall design can not only accurately capture changed areas, but also restore resolution during the upsampling process, realizing efficient and accurate remote sensing image change detection.

[0142] Step 5: Use the mixed loss function L to predict the change graph Parameter optimization is performed, as detailed below;

[0143] L=L wce +L dice

[0144]

[0145] Among them, L is the total mixed loss function, that is, the weighted cross entropy loss L wce and Dice loss L dice of and;

[0146] It is the predicted change graph An element in represents the prediction result of a specific position in the graph.

[0147] Y is the true change graph,

[0148] L wce is the weighted cross entropy loss,

[0149] L dice for Dice loss;

[0150] In this step, the weighted cross entropy loss L is used wce and Dice loss L diceThe hybrid loss function is optimized. The weighted cross entropy loss can deal with the problem of class imbalance and enhance the model's ability to identify minority classes. The Dice loss focuses on the overlapping area between the predicted image and the true change image, improving the accuracy of the model in the change area. The design of the hybrid loss ensures that the model can not only cope with class imbalance but also accurately capture the change area, thereby improving the overall effect of remote sensing image change detection.

[0151] Among them, in the field of change detection, since the number of unchanged pixels is often much larger than the number of changed pixels, in order to reduce the impact of sample imbalance, a hybrid loss function is usually used, that is, a combination of weighted cross entropy loss and Dice loss. The cross entropy loss handles the sample imbalance problem by adjusting the category weights, calculates the loss of each pixel, and averages the loss values of all pixels. The Dice loss measures the degree of overlap between the prediction and the true annotation, optimizes the model to increase the overlap, and finally optimizes the parameters through the hybrid loss function, thereby improving the prediction performance and accuracy of the model.

[0152] Verification experiment

[0153] like Figure 2 、 Figure 3 and Figure 4 As shown, the effect of hyperspectral change detection can be shown, where Figure 2 and Figure 3 are hyperspectral images of the same area at two different time points. Figure 4 To use the change detection results of the method of the present invention, Figure 5 It is a true value map of actual ground changes; from the detection results and the true value map of actual ground changes, it can be seen that the method proposed in the present invention can effectively identify the changed area and improve the accuracy of remote sensing image change detection.

[0154] In summary, the present invention adaptively mixes the CNN module and the Transformer module, utilizes the advantages of the CNN module in extracting local feature information of the image, and the advantages of the multi-scale Transformer module in extracting global feature information of the image, so as to better integrate all the global and local information of the high-resolution remote sensing image in the feature extraction stage, thereby ensuring the richness of the feature information of the feature extraction layer. At the same time, a multi-scale attention mechanism is used in the U-shaped decoder to perform dimensionality reduction operations on high-dimensional features by extracting and fusing features at different scales. This not only reduces the complexity of attention calculation, but also effectively solves the limitation that single-scale feature extraction is often difficult to fully capture the information in high-resolution remote sensing images, ensuring that the pixel detail information of the high-resolution remote sensing image can be retained, thereby accurately identifying the changes in the high-resolution remote sensing images of the same area at different times, ensuring the expression of shallow and deep information of the high-resolution remote sensing images, improving the accuracy of change detection of high-resolution remote sensing images, and having low detection calculation complexity and high detection efficiency.

[0155] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A transformation detection method for remote sensing images based on multi-scale global-local feature fusion, characterized by: The following steps are involved: Step 1: Input two high-resolution remote sensing images of the same area at different times and group them by channel, as follows: 1.1, let T 1 Represents the remote sensing image of the previous phase, T 2 stands for post-temporal remote sensing image; 1.2、T i Divide by channel into X 1 ,X 2 Two groups; Step 2: Extract and merge features of the grouped high-resolution remote sensing images to obtain global and local feature information at multiple scales; Among them, taking the first ConvTransBlock module as the benchmark, the specific steps to obtain the global local feature information F1 are as follows: 2.1、X 1 Input to the CNN module with a convolutional layer size of 3×3 and downsample it, and extract X 1 Local feature information The specific formula is as follows: Among them, Conv(·) is the convolution operation; 2.2, X 2 After downsampling through the 1×1 convolutional layer, the input is fed into the Transformer module to extract X 2 Global feature information The specific formula is as follows: F Z =Conv(X 2 ) Where MST(·) represents the Multiscale Transformer module; 2.3 Local feature information and global feature information Perform channel splicing to obtain global and local feature information F 11 , the specific formula is as follows: in, Represents channel splicing operation; Step 3: The output of the global and local feature information at different scales in the ConvTrans module is input into the CC fusion module for feature fusion and output to obtain f′, f″, f″′, and f″″, which are specifically: The outputs of the ConvTrans module at different scales are input to the CC fusion module for channel splicing, and a 1×1 convolution is performed to obtain f′, f″, f″′, and f″"; Step 4: Input f′, f", f″′, and f″″ into the multi-scale attention decoder of the U-shaped structure to obtain global and local information at multiple scales and generate a predicted change map. Step 5: Use the mixed loss function L to predict the change graph Perform parameter optimization.

2. The method for detecting transformation of remote sensing images based on multi-scale global-local feature fusion according to claim 1, characterized in that: In step 1.1: T∈{T i ∈R C×H×W |i=1,2}; Where H is the height of the high-resolution remote sensing image, W is the width of the high-resolution remote sensing image, C represents the number of channels of the high-resolution remote sensing image, that is, there are C channels; In step 1.2: T i Divide by channel into X 1 ,X 2 The two groups, the grouping results are: X 1 =ChannelSplit(T i ) X 2 =ChannelSplit(T i ) Among them, ChannelSplit() is a channel splitting operation.

3. The method for detecting transformation of remote sensing images based on multi-scale global-local feature fusion according to claim 1, characterized in that: In step 2, feature extraction and feature merging are performed on the grouped high-resolution remote sensing images to obtain global and local feature information at multiple scales, that is, T 1 、T 2 X 1 ,X 2 The outputs are input into the hybrid module of Transformer module and CNN module respectively, and the outputs of the two modules are combined to obtain global and local feature information at multiple scales; Among them, the global and local feature information at multiple scales includes T 1 The global and local feature information F 11 , global and local feature information F 21 , global and local feature information F 31 , global and local feature information F 41 ; and T 2 The global and local feature information F 12 , global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 .

4. The method for detecting transformation of remote sensing images based on multi-scale global-local feature fusion according to claim 3, characterized in that: In step 2, the global local feature information F is obtained 21 , global and local feature information F 31 , global and local feature information F 41 The specific steps are as follows: 2.

4. Taking the second ConvTransBlock module as the benchmark, the global local feature information F 11 Input into the ConvTransBlock module, group by channel, and repeat steps 2.1-2.3 to obtain the global and local feature information F 21 ; 2.5, based on the third ConvTransBlock module, the global local feature information F2 is input into the ConvTransBlock module, grouped by channel, and steps 2.1-2.3 are repeated to obtain the global local feature information F 31 ; 2.6, based on the fourth ConvTransBlock module, the global local feature information F3 is input into the ConvTransBlock module, grouped by channel, and steps 2.1-2.3 are repeated to obtain the global local feature information F 41 .

5. The method for detecting transformation of remote sensing images based on multi-scale global-local feature fusion according to claim 4, characterized in that: In step 2, we get T 2 The global and local feature information F 12 , global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 The specific steps are: Repeat steps 2.1-2.6 to obtain the global and local feature information F 12 , global and local feature information F 22 , global and local feature information F 32 , global and local feature information F 42 ; Among them, T 1 、T 2 The outputs of the hybrid network of the Transformer module and CNN module of the two branches at four different stages are 6. The method for detecting transformation of remote sensing images based on multi-scale global-local feature fusion according to claim 1, characterized in that: In step 3, the outputs of the ConvTrans module at different scales are input into the CC fusion module for channel splicing, and a 1×1 convolution is performed to obtain f′, f″, f″′, and f″″. The specific formula is as follows; 7. The method for detecting transformation of remote sensing images based on multi-scale global-local feature fusion according to claim 1, characterized in that: In step 4, f′, f″, f″′, and f″″ are input into the multi-scale attention decoder of the U-shaped structure to obtain the global and local information at multiple scales and generate a predicted change map. The specific steps are as follows: 4.

1. Input f' into the MSA module and downsample to obtain output D1. Add D1 and f'' to obtain D1', which is then input into the MSA module for downsampling to obtain D2. Add D2 and f'' to obtain D2', which is then input into the MSA module for downsampling to obtain D3. Add D3 and f'' to obtain D3', which is then input into the MSA module for downsampling to obtain D4. The specific formula is as follows: Among them, MSA(·) represents the multi-scale attention module; 4.

2. Upsample and merge D1, D2, D3, and D4 and input them into the corresponding ECA module to generate a predicted change graph. The specific formula is as follows: Among them, d i i={1, 2, 3, 4} in ′, Among them, ECA(·) is the operation of the ECA module, is the matrix multiplication operation, Up(·) is an upsampling operation.

8. The method for detecting transformation of remote sensing images based on multi-scale global-local feature fusion according to claim 7, characterized in that: In step 5, the hybrid loss function L is used to predict the change map Parameter optimization is carried out, and the specific details are as follows: Among them, L is the total mixed loss function, that is, the weighted cross entropy loss L wce and Dice loss L dice of and; It is the predicted change graph An element in represents the prediction result of a specific position in the graph. Y is the true change graph, L wce is the weighted cross entropy loss, L dice Dice loss.

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