RoCD: leveraging foundation vision models with refine-and-fuse framework for robust change detection

In recent years, Foundation Vision Models (FVMs) have provided new technical approaches for change detection and understanding of remote sensing images due to their strong generalization and multi-scale representation capabilities. However, in complex spatiotemporal scenarios, existing methods still face two major challenges: insufficient feature interaction and an imbalance between global and detail representation. To address these challenges, this paper proposes RoCD, which introduces the Refine-and-Align Framework (RAF) and FusionR-Decoder on top of the frozen basic visual model, FastSAM encoder. First, RAF introduces a pairwise difference refinement (PDR) mechanism to enhance feature interactions and effectively suppress spurious changes caused by inter-domain differences. Second, FusionR-Decoder embeds a three-branch RBlock based on state space models (SSMs) in the multi-scale decoding stage to achieve long dependency modeling and global consistency constraints. Experimental results on the three public datasets LEVIR-CD, LEVIR-CD+, and WHU-CD show that RoCD achieves F1/IoU scores of 92.11/85.38, 87.68/76.90, and 95.95/91.04, respectively.