## 3 Methodology  
### Overview  
As shown in Figure 1, the MDNet proposed in this paper is a siamese network architecture that fuses a CNN, multi-scale feature aggregation, differential perception enhancement,Transformer, and attention mechanisms. The network employs an improved ResNet [45] as the backbone, performs multi-scale feature extraction on bi-temporal images through the EMF, and constructs a feature pyramid to capture representations of ground objects with different sizes. The CASAM is utilized to achieve interactive aggregation of cross-layer adjacent semantic features, enhancing the representation of ground object structure information and semantic consistency. Subsequently, the aggregated features are subjected to spatio-temporal context modeling via Transformer modules and attention mechanisms, capturing long-range dependencies through self-attention mechanisms. Meanwhile, the DPEM constructs a spatio-temporal differential feature space to explicitly enhance the spectral-structural difference representation of changed areas. The output features of DPEM are further processed by Transformer and attention mechanisms to deepen the context modeling of the difference domain, effectively bridging the inter-domain differences between bi-temporal images. Specifically, let \(T_{1}\) and \(T_{2}\) represent images of the same area taken at two different times, respectively. \(I_{i}(i\in\{1,2\})\in R^{3\times H\times W}\) represents the original feature map of image \(T_{i}(i\in\{1,2\})\). The MDNet process is summarized below:  
Figure 1: Architecture of the proposed MDNet. Information Flow: Bi-temporal images are input to share the ResNet50 backbone, and multi-scale features are generated through the EMF; after being fed into the CASAM for cross-scale aggregation, they are then input into the DPEM to generate differential features; the differential features and aggregated features are jointly processed by the Transformer and CAM, and finally undergo concatenation and classification via a CNN classifier, outputting 3 change maps. (i) First, for the input images \(I_{i}(\forall i\in\{1,2\})\in R^{3\times H\times W}\), three sets of multi-scale feature maps \(G_{i}^{(1)},G_{i}^{(2)},G_{i}^{(3)}\) are extracted through the shared-parameter ResNet backbone network. (ii) Next, adjacent-scale feature maps are input into the Cross-Scale Adjacent Semantic Information Aggregation Module (CASAM), and feature fusion is performed on \(G_{1}^{j(1)}\) and \(G_{2}^{j(1)}(\forall j\in\{1,2,3\})\) of the two branches, respectively, generating optimized multi-scale feature maps \(G_{i}^{j(2)}(\forall i\in\{1,2\},\forall j\in\{1,2,3\})\). (iii) Then, the feature maps \(G_{i}^{j(2)}(\forall i\in\{1,2\},\forall j\in\{1,2,3\})\) of the same scale in the bi-temporal images are input into the Differential Perception Enhancement Module (DPEM), and spatio-temporal differential feature maps \(G_{3}^{j(3)}(\forall j\in\{1,2,3\})\) are extracted. (iv) After that, \(G_{3}^{j(3)}(\forall j\in\{1,2,3\})\) and \(G_{i}^{j(2)}(\forall i\in\{1,2\},\forall j\in\{1,2,3\})\) are sequentially input into the Transformer and Channel Attention Module (CAM), generating feature maps \(G_{i}^{j(4)}(\forall i,j\in\{1,2,3\})\) that integrate spatio-temporal information. (v) Meanwhile, the multi-scale Feature Extraction Module (EMF) processes the ResNet backbone features to generate feature maps \(I_{3},I_{4}\). After unifying the channel dimensions through convolutional layers, they are input into the DPEM and CAM to obtain the feature map \(I_{5}\), which is added to \(G_{3}^{3(4)}\) to achieve feature updating. (vi) Finally, the updated multi-scale feature maps \(G_{i}^{j(4)}(\forall i,j\in\{1,2,3\})\) are concatenated along the channel dimension and input into the corresponding CNN classifiers to obtain three predicted change maps.