Multi-Scale Co-Attention Learning For SAR Image Change Detection

Synthetic aperture radar (SAR)iamge change detection is an important and challenging task. Existing methods mainly extract features from the difference map and original images in a parallel structure, ignoring the correlations between features. In this paper, we propose a novel SAR image change detection method based onmulti-scale and co-attention learning, called the Multi-scale Co-attention Network (MCNet). Specifically, we extract features from the difference map and original images using co-attention mechanism, which establishes a relationship between the two kinds of features in the feature space while retaining key information from the original images. With this attention mechanism, the model pays more attention to the changed areas in SAR images, and the features contain richer information. Furthermore, we propose a multi-scale feature fusion method to combine high-level and low-level semantic information, improving the generality and robustness of the features. Finally, the performance of the proposed method has been validated for its effectiveness.