Difference-Gated Interaction and Change-Aware Cross-Temporal Fusion Network for Remote Sensing Image Change Detection

Remote sensing (RS) image change detection (CD) is crucial for environmental monitoring, urban planning, and disaster assessment. Despite recent advances, existing methods struggle to effectively exploit bitemporal difference information, leading to false alarms caused by illumination or seasonal variations. Furthermore, they often fail to fully leverage coarse predictions as explicit semantic priors, restricting their capability to detect small-scale change regions. To address these issues, we propose a Difference-Gated Interaction and Change-Aware Cross-Temporal Fusion Network (DCAFNet) within a unified coarse-to-fine framework. Specifically, a Difference-Gated Feature Interaction (DGFI) module generates change correlation gates based on difference magnitudes to suppress pseudo-changes while preserving genuine change signals, and a Change-Aware Cross-Temporal Fusion (CCTF) module leverages coarse predictions as semantic guidance for feature recalibration and employs cross-temporal attention with learnable adaptive fusion to recover subtle change regions. Extensive experiments on four benchmark datasets (LEVIR-CD, WHU-CD, CDD, and SYSU-CD) demonstrate that DCAFNet consistently outperforms ten state-of-the-art methods, with additional ablation studies confirming the effectiveness of each component.