Feature Enhancement and Feedback Network for Change Detection in Remote Sensing Images

Remote sensing change detection (CD) has garnered extensive research and application due to its ability to identify changes in land features within the same area across different periods. CD tasks require features with strong intraclass distinctions and precise spatial boundary details. Existing methods enhance the extraction of difference features but significantly increase computational complexity in high-resolution remote sensing imagery. Moreover, these methods focus on pixel-level difference extraction while neglecting feedback from the overall change object. As a result, they lack global information perception, leading to blurred edges and fragmented interiors in the change areas. To address these challenges, we propose a feature enhancement and feedback network (FEFNet) for CD. First, we designed a multilevel dual-feature fusion enhancement module (DFFM) to improve the representation of latent features between the bitemporal images. Second, we developed a feature coupling feedback module (FCFM) that efficiently decodes multiscale change features to generate extraction results. The experimental results show that FEFNet outperforms recent models in both computational efficiency and detection performance. With only 10.56G FLOPs and 2.26M parameters, FEFNet achieves an ${F}_{1}$ score of 92.32% on the LEVIR-CD dataset and 93.77% on the WHU-CD dataset. The code will be available at https://github.com/XiaoJ058/RS-CD.