SADER：一种结构感知扩散框架，用于多时相遥感云去除
SADER: Structure-Aware Diffusion Framework with DEterministic Resampling for Multi-Temporal Remote Sensing Cloud Removal
AI总结：
SADER通过结构感知扩散框架，结合时间融合和混合注意力机制，有效解决多时相遥感云去除问题，提升云去除效果和效率。
摘要：
云污染严重地降低了遥感图像的可用性，并对下游地球观测任务构成了根本性挑战。最近，扩散模型因其强大的生成能力和稳定的优化能力，已成为遥感云去除的主导范式。然而，现有的扩散模型方法往往在多时相遥感场景中存在采样效率有限和对结构和时间先验利用不足的问题。在本文中，我们提出了SADER，一种用于多时相遥感云去除的结构感知扩散框架。SADER首先开发了一个可扩展的多时相条件扩散网络（MTCDN），通过时间融合和混合注意力机制，全面捕捉多时相和多模态相关性。然后，引入了一种云感知的注意力损失，通过考虑云厚和亮度差异，强调云主导区域。此外，设计了一种确定性重采样策略，用于连续扩散模型，在固定采样步骤下通过引导修正替换异常值来迭代优化样本。在多个多时相数据集上的大量实验表明，SADER在所有评估指标上都优于最先进的云去除方法。SADER的代码已公开在https://github.com/zyfzs0/SADER。
英文摘要：
Cloud contamination severely degrades the usability of remote sensing imagery and poses a fundamental challenge for downstream Earth observation tasks. Recently, diffusion-based models have emerged as a dominant paradigm for remote sensing cloud removal due to their strong generative capability and stable optimization. However, existing diffusion-based approaches often suffer from limited sampling efficiency and insufficient exploitation of structural and temporal priors in multi-temporal remote sensing scenarios. In this work, we propose SADER, a structure-aware diffusion framework for multi-temporal remote sensing cloud removal. SADER first develops a scalable Multi-Temporal Conditional Diffusion Network (MTCDN) to fully capture multi-temporal and multimodal correlations via temporal fusion and hybrid attention. Then, a cloud-aware attention loss is introduced to emphasize cloud-dominated regions by accounting for cloud thickness and brightness discrepancies. In addition, a deterministic resampling strategy is designed for continuous diffusion models to iteratively refine samples under fixed sampling steps by replacing outliers through guided correction. Extensive experiments on multiple multi-temporal datasets demonstrate that SADER consistently outperforms state-of-the-art cloud removal methods across all evaluation metrics. The code of SADER is publicly available at https://github.com/zyfzs0/SADER.
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。
