## 3 Methodology
### Differential-Perception-Enhanced Module (DPEM)  
In the task of remote sensing change detection, the importance of each pixel in the image varies. Change targets such as building changes and wasteland changes need to be focused on, and these change areas usually only account for a small part of the image. Most traditional methods directly obtain differential semantics through pixel-level subtraction to extract change features. However, this approach hinders the network from learning the information of the difference map. Based on this, we propose the Differential-Perception-Enhanced Module (DPEM) to construct advanced differential semantic representations through a multi-dimensional feature regulation mechanism. First, the module constructs a cross-channel information interaction network with the help of a lightweight channel attention mechanism, adaptively enhancing the spectral-textural feature responses related to the change detection task, while suppressing redundant channel information to improve the pertinence of feature expression. Then, it uses the Spatial Attention module (SAM) to dynamically adjust the importance of each spatial position in the feature map. Finally, pixel-level subtraction is performed on the feature maps at two time instants to obtain high-quality feature differences, effectively suppressing the interference from background regions and highlight changes.  
As shown in Figure 4a, the specific implementation of the DPEM is as follows: First, the global pooling unit is used to perform mapping and transmission on the input features \(Input_{i}(i=1,2)\). Subsequently, a one-dimensional convolution is used to capture local cross-channel interactions and avoid the dimensionality reduction in the fully connected layer. Then, the feature map passes through the sigmoid layer to obtain a \(1\times 1\times C\) channel attention mask. Finally, the channel attention mask is multiplied by the input feature map to obtain the channel attention feature map \(M_{i}^{1}\), as shown in Equation (4):  
\[M_{i}^{1}=Input_{i}\otimes\sigma(Conv1D_{k}(Avg(Input_{i}))) \tag{4}\]  
where \(Input_{i}\) represents the input feature map \((i=1,2)\), \(Avg(\cdot)\) represents global average pooling, \(Conv1D_{k}(\cdot)\) represents a one-dimensional convolution with a kernel size of \(k=5\), \(\sigma(\cdot)\) represents the sigmoid activation function, and \(\otimes\) represents element-wise multiplication. \(M_{i}^{1}\) represents the channel attention feature map (\(i=1,2\)).  
To obtain the spatial attention feature map \(M_{i}^{2}(i=1,2)\), average pooling, max-pooling, \(7\times 7\) convolution operation, and sigmoid operation need to be performed on \(M_{i}^{1}\), as shown in Equation (5):  
\[M_{i}^{2}=M_{i}^{1}\otimes\sigma(f_{7\times 7}([Max(M_{i}^{1});Avg(M_{i}^{1})])) \tag{5}\]  
where \(Max(\cdot)\) represents max-pooling, \(f_{7\times 7}(\cdot)\) represents a two-dimensional convolution with a kernel size of 7, and \(M_{i}^{2}\) represents the spatial attention feature map \((i=1,2)\).  
Finally, pixel-level subtraction is performed on the channel-spatial feature maps \(M_{i}^{2}(i=1,2)\) containing the input feature maps at different times to obtain the differential-enhanced feature map \(output\), as shown in Equation (6):  
\[output=\left|M_{1}^{2}-M_{2}^{2}\right| \tag{6}\]