Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection

Remote sensing change detection is often complicated by spatial misalignment between image pairs, especially when observations are separated by long temporal gaps such as seasonal or multi-year intervals. Conventional CNN- and transformer-based methods perform well on aligned data, but their reliance on perfect co-registration limits their applicability in practice. Existing approaches that integrate registration and change detection generally demand task-specific training and transfer poorly across domains. We present a lightweight, modular pipeline that strengthens robustness without retraining the underlying change detection models. The framework combines rapid per-image LoRA adaptation with a compact flow refinement module trained under supervision. To mitigate large appearance differences, we generate intermediate morphing frames via a diffusion-based semantic interpolator. Consecutive frames are aligned using a registration backbone (e.g., RoMa), and the composed flows are further corrected through a residual refinement network. The refined flow is then applied to co-register the original image pairs, enabling more reliable downstream change detection. Extensive experiments on LEVIR-CD, DSIFN-CD, and WHU-CD demonstrate that the proposed pipeline significantly improves both registration accuracy and change detection performance, especially in scenarios with substantial spatial and temporal variations.