High-Resolution Remote Sensing Change Detection With Edge-Guided Feature Enhancement

Abstract

High-resolution (HR) remote sensing image change detection aims to identify surface changes; however, complex scenes and irregular object edges pose significant challenges to achieving accurate results. Existing methods leverage upsampling, downsampling, or dilated convolution to capture multiscale spatial features and fuse fine-scale details into coarse-scale features using concatenation, addition, or skip connections to enhance edge information. However, these direct fusion operations can cause fine edge details to be overshadowed by dominant regional features. To address this, we propose an edge-guided change detection (EGCD) network that improves edge preservation and detection accuracy. In the encoding stage, a region-edge feature extraction module (REM) is introduced to extract regional and edge features in parallel using a two-branch structure for each temporal image. The edge and regional features from the two temporal images are then fused independently via a separation feature fusion (SFF) module, preventing fine edge details from being dominated by regional features. In the decoding stage, a edge enhancement upsampling (EEU) module uses edge features to guide the reconstruction of regional features, ensuring precise boundary delineation. Experiments on public datasets validate the effectiveness and robustness of the proposed network.