CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD) is essential for monitoring land use, urban expansion, and environmental changes. Despite advancements in deep learning, existing methods exhibit limitations such as discontinuous edges and internal gaps in detected change regions, primarily due to insufficient feature fusion and limited contextual understanding. In this paper, we propose a novel change indicator-enhanced multiscale fusion network,called CIMF-Net,which integrates multiscale feature fusion modules (MFFM) and cascade guided attention modules (CGAM) to overcome these limitations.MFFM facilitates the extraction of semantic information through multiscale features, while CGAM employs hierarchical attention mechanisms to refine feature representation and enhance change region localization. Extensive experiments on three benchmark datasets highlight the superior performance of CIMF-Net compared to baseline network, achieving higher F1-scores, IoU, and overall accuracy. Furthermore, ablation studies confirm the effectiveness of MFFM and CGAM in enhancing feature fusion and hierarchical guidance.Our method not only delivers robust results in challenging scenarios but also presents a new perspective and technology for change detection.