随时间变形：基于扩散的时间间隙桥接用于变化检测中的鲁棒对齐
Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection
AI总结：
针对遥感变化检测中双时相图像空间错位问题，提出集成扩散语义变形、密集配准和残余流细化的模块化流水线，无需修改现有检测网络即可提升时空鲁棒性，在多个数据集和骨干网络上实现配准精度与检测性能的持续提升。
摘要：
遥感变化检测常面临双时相图像间的空间错位挑战，尤其当数据采集间隔较长的季节或多年时间间隙时。尽管现代卷积和基于Transformer的模型在对齐数据上表现良好，但它们对精确配准的依赖限制了其在现实条件下的鲁棒性。现有联合配准-检测框架通常需要重新训练，且跨域迁移性差。我们提出一种模块化流水线，无需修改现有变化检测网络即可提升空间和时间鲁棒性。该框架集成了基于扩散的语义变形、密集配准和残余流细化。扩散模块合成中间变形帧以桥接大的外观间隙，使RoMa能估计连续帧间的逐步对应关系。合成的流随后通过轻量级U-Net细化，生成高保真变形以配准原始图像对。在LEVIR-CD、WHU-CD和DSIFN-CD上的大量实验表明，该方法在多个骨干网络上持续提升配准精度和下游变化检测性能，证明了其通用性和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
