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 pseudo-changes. To this end, a novel Multi-scale 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 (PCAM) 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 pseudo-changes. These two components work together to advance the performance of MEPNet. Experimental results demonstrate that our method outperforms eleven SOTA methods on the LEVIR-CD, SYSU-CD, and CDD datasets, achieving superior accuracy and efficiency.