SCF-Net: A Flow-Guided Alignment-Enhanced SAM–CNN Hybrid Framework for Remote Sensing Change Detection

Abstract

While integrating convolutional neural networks (CNNs) and the segment anything model (SAM) is promising for remote sensing change detection (CD), effectively synergizing them remains challenging. Existing hybrid methods often rely on simple feature concatenation, failing to bridge the gap between CNNs’ fine-grained local structures and SAM’s global semantic priors. Moreover, neglecting spatial misalignment in bi-temporal imagery often leads to pseudo-changes and boundary inconsistencies. To address these limitations, we propose SCF-Net, a flow-guided alignment-enhanced framework. First, a dual-path fusion module (DPFM) bridges the cross-modal semantic gap by embedding global contexts into local representations. Second, an optical-flow-guided differential enhancement module (OFDEM) implements adaptive flow-based warping to rectify spatial shifts. Finally, a cross-scale fusion module (CSFM) ensures hierarchical feature consistency. Extensive experiments across LEVIR-CD, CLCD, and GFSW-CLCD demonstrate that SCF-Net effectively mitigates granular mismatch and registration noise. The proposed model achieves competitive $F1$ -scores of 91.58%, 81.23%, and 83.80% on the three datasets, respectively, demonstrating its effectiveness and robustness compared to current mainstream algorithms.