Global–Local Feature Collaborative Fusion and Edge Refinement Network for Remote Sensing Change Detection

Remote sensing image change detection (RSI-CD) has witnessed significant progress. The core focus lies in accurately identifying spatio-temporal changes in remote sensing images and precisely extracting edge details. However, current remote sensing image change detection primarily faces two challenges: the diversity and complexity of change regions, and the blurred edges of change regions. These issues lead to pseudo-change interference and incomplete detection of change targets. To alleviate these limitations, this study first develops a Local-Global Differential Fusion Module (LGDFM) that integrates local feature extraction with global self-attention mechanism to enhance change feature representation through effective bi-temporal image fusion. This module simultaneously captures both global and local information from source images, thereby overcoming the insufficient feature fusion in existing bi-temporal change detection methods. Subsequently, we propose an Edge-Aware Enhancement Module (EAEM) that employs morphological operations to extract edge features. The module progressively refines prediction results through multi-stage edge feature integration that ensures precise contour delineation. Experiments on two datasets demonstrate that the proposed method achieves competitive performance compared to existing approaches.