GFMNet: A Frequency-Aware Feature Fusion Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD) is essential for monitoring surface changes, requiring features that capture both categorical and fine boundary details at high resolution. Deep learning methods typically fuse upsampled coarse features from deep layers with high-resolution shallow features to form comprehensive semantic representations. However, the fixed grid structure in Euclidean space limits the handling of irregular boundaries and edges, often resulting in blurred boundaries, especially near ambiguous thresholds. To overcome these limitations, we propose a graph neural network (GNN)-based feature fusion method that integrates high- and low-frequency information with a mutual information-driven semantic enhancement module. Specifically, we leverage non-Euclidean space to enable flexible spatial transformations during feature extraction. In our framework, low-frequency extraction captures global structure and background features for intraclass consistency, while high-frequency extraction preserves edge details for boundary clarity. After fusing these complementary features, we further optimize their mutual information to suppress background noise and irrelevant changes, enabling the model to focus on true change regions and thereby improving detection accuracy and overall performance. The code is available at https://github.com/sundaobo/GFMNet