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

TL;DR

This paper presents a modular diffusion-based framework that enhances the robustness of change detection in remote sensing by improving spatial and temporal alignment across long gaps without retraining existing models.

Contribution

It introduces a novel pipeline combining diffusion-based semantic morphing, dense registration, and residual flow refinement to improve alignment in change detection tasks.

Findings

Consistent improvements in registration accuracy across datasets.

Enhanced change detection performance with various backbones.

Robustness to large appearance gaps in multi-temporal images.

Abstract

Remote sensing change detection is often challenged by spatial misalignment between bi-temporal images, especially when acquisitions are separated by long seasonal or multi-year gaps. While modern convolutional and transformer-based models perform well on aligned data, their reliance on precise co-registration limits their robustness in real-world conditions. Existing joint registration-detection frameworks typically require retraining and transfer poorly across domains. We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks. The framework integrates diffusion-based semantic morphing, dense registration, and residual flow refinement. A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences between consecutive frames. The composed flow is then refined through a lightweight U-Net to produce a high-fidelity warp that co-registers the original image pair. Extensive experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection across multiple backbones, demonstrating the generality and effectiveness of the proposed approach.