Enhancing Change Detection With Edge-Guided Difference Modeling in Remote Sensing Imagery

Change detection (CD) in remote sensing imagery remains challenging due to boundary ambiguity and false alarms caused by high foreground-background similarity and insufficient difference representation. To address these issues, we propose an Edge-Guided Difference Enhancement Network (EGDENet). EGDENet integrates an edge-aware Adaptive Enhancement Module (EAEM) to extract high-frequency edge cues across scales, and a Channel-Spatial Cooperative Difference Module (CSCDM) to refine change features by jointly leveraging spatial and channel-wise differences. An Up-sampling Feature Fusion (UFF) further enhances robustness to scale variations and improves region consistency. Extensive experiments on two public datasets demonstrate that EGDENet achieves superior performance with clearer boundaries compared to state-of-the-art methods. Our source code is publicly available at https://github.com/adleess/-EGDENet.