# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 3 Principles and Methods  
Our proposed AMMNet is a typical multi-scale twin network consisting of five components: ResNet, HFAM, the feature exchange module, the SDAM, and the classifier. ResNet serves as the backbone of the coastal zone change detection network, which is responsible for extracting multi-scale features from the dual-temporal input images. By removing the initial fully connected layers, it helps reduce information loss. The HFAM is designed to integrate multi-scale features, leveraging spatial attention and introducing the Sobel operator to selectively enhance the high-frequency information of buildings in the building change detection task. This enables the network to capture different levels of predictable coastal zone change patterns, improving the overall performance of complex coastal zone change detection while minimizing the interference of redundant information and irrelevant features. The feature exchange module merges multi-layered features, balancing both detail and semantic information, thus improving the model's change detection capability while simplifying computation and optimizing gradient flow. The SDAM helps the model gradually focus on the change regions by integrating and processing feature maps at different scales, enhancing the learning of change information and alleviating the issue of category imbalance. Finally, the classifier generates the change result map by applying a thresholding mechanism. As shown in Figure 1, let \(\text{X}_{1}\in\text{R}^{3\times\text{H}\times\text{W}}\) and \(\text{X}_{2}\in\text{R}^{3\times\text{H}\times\text{W}}\) denote the pre- and post-event remote sensing images of the same coastal zone area taken at different times. The change detection method follows the three steps outlined below:  
Figure 1: Illustration of the AMMNet we proposed. Step 1 (edge feature extraction): The two images \(\mathrm{X_{i}}(\forall\mathrm{i}\in\{1,2\})\in\mathrm{R}^{3\times\mathrm{H}\times \mathrm{W}}\) are passed through the ResNet backbone, and three feature maps at different scales \(\mathrm{F_{i}^{1(1)}}\), \(\mathrm{F_{i}^{2(1)}}\), and \(\mathrm{F_{i}^{3(1)}}(\forall\mathrm{i}\in\{1,2\})\) are extracted from each image. These feature maps are then swapped with the feature maps of the same scale from the other branch of the twin. Network to minimize the domain gap between images from different time periods. After the exchange, the feature maps \(\mathrm{F_{i}^{(2)}}(\forall\mathrm{i}\in\{1,2,3\},\forall\mathrm{i}\in\{1,2\})\) are passed into the high-frequency attention module (HFAM), which integrates the commonly used spatial and channel attention mechanisms and introduces the Sobel operator in the channel attention to enhance the model's feature extraction ability. This helps to obtain clearer edge features and improves the model's sensitivity to rapidly changing regions (such as edges, textures, and details) in coastal zone change detection. Step 2 (integration of contextual information): First, the three high-frequency feature maps \(\mathrm{F_{i}^{(2)}}\) from the same branch are rescaled to the same spatial scale. The feature maps at different scales are then fused using element-wise summation to obtain combined feature maps \(\mathrm{F_{i}^{(3)}}(\forall\mathrm{i}\in\{1,2,3\},\forall\mathrm{i}\in\{1,2\})\), which facilitates the full utilization of both local detail and global contextual information. These two types of information complement each other, improving detection accuracy, especially at the boundaries of changing regions. Next, the feature maps from different branches, but at the same scale, are grouped into three pairs and passed into the spatio-temporal disparity attention module (SDAM) to obtain feature maps \(\mathrm{F^{i(4)}}(\forall\mathrm{i}\in\{1,2,3\})\), which integrates both spatio-temporal global information and local contextual information through its dual-branch structure. This effectively enhances the representation of change-related regions while suppressing irrelevant interference.