DiffRegCD: Integrated Registration and Change Detection with Diffusion Features

Change detection (CD) is critical in computer vision and remote sensing, with applications in monitoring, disaster response, and urban analysis. Most CD models assume co-registered inputs, but real imagery often suffers from parallax, viewpoint shifts, or long temporal gaps, leading to severe misalignment. Conventional register-then-detect pipelines and recent joint frameworks (e.g., BiFA, ChangeRD) remain limited: they rely on regression-only flow, global homographies, or synthetic perturbations that fail under large displacements. We propose DiffRegCD, an integrated framework that couples dense registration and change detection. DiffRegCD reformulates correspondence as a Gaussian-smoothed classification task, delivering sub-pixel accuracy and stable training. It builds on frozen multi-scale features from a pretrained denoising diffusion model, which provide invariance to viewpoint and illumination variation. Supervision is enabled by controlled affine perturbations applied to standard CD datasets, yielding paired ground truth for both flow and change detection without pseudo-labels. Experiments on aerial (LEVIR-CD, DSIFN-CD, WHU-CD, SYSU-CD) and ground-level (VL-CMU-CD) datasets show that DiffRegCD outperforms recent baselines and remains robust under wide temporal and viewpoint variation, establishing diffusion features and classification-based correspondence as a strong foundation for integrated CD.