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

📝 Original Info

• Title: Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection
• ArXiv ID: 2511.07976
• Date: 2025-11-11
• Authors: - 논문에 명시된 저자 정보가 제공되지 않았습니다. (저자명 및 소속을 확인하려면 원문 PDF 혹은 학술 데이터베이스를 참고하시기 바랍니다.)

📝 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.

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