Multiscale Edge Enhancement and Progressive Change-Aware Network for Remote Sensing Change Detection

Change detection (CD) in remote sensing (RS) images serves as a vital method for identifying changes on the Earth’s surface. Recent advancements in deep learning (DL)-based CD methods have shown considerable progress. However, there is still significant room for further improvement of CD performance, particularly in fine-grained detection, such as enhancing edge details and reducing pseudochanges. To this end, a novel multiscale edge enhancement and progressive change-aware network (MEPNet) is proposed to improve the ability of feature representation for changed objects. Specifically, we introduce an edge enhancement module (EEM) to capture the long-range dependency, explicitly emphasizing high-frequency feature, and strengthening edge information to improve the accuracy of change regions. In addition, we propose a progressive change-aware module that progressively applies depthwise separable convolutions with kernels of decreasing size to localize changes at different scales, enabling precise refinement of change objects and reducing pseudochanges. These two components work together to advance the performance of MEPNet. Experimental results demonstrate that our method outperforms 11 SOTA methods on the LEVIR-CD, SYSU-CD, and CDD datasets, achieving superior accuracy and efficiency. The source code can be found at https://github.com/take-off-xyz/MEPNet