Driven by rapid climate change, the frequency and intensity of flood events are increasing. Electro-Optical (EO) satellite imagery is commonly utilized for rapid response. However, its utilities in flood situations are hampered by issues such as cloud cover and limitations during nighttime, making accurate assessment of damage challenging. Several alternative flood detection techniques utilizing Synthetic Aperture Radar (SAR) data have been proposed. Despite the advantages of SAR over EO in the aforementioned situations, SAR presents a distinct drawback: human analysts often struggle with data interpretation. To tackle this issue, this paper introduces a novel framework, Diffusion-Based SAR to EO Image Translation (DSE). The DSE framework converts SAR images into EO images, thereby enhancing the interpretability of flood insights for humans. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework not only delivers enhanced visual information but also improves performance across all tested flood segmentation baselines.

翻译：受快速气候变化驱动，洪水事件的频率和强度正在增加。光电（EO）卫星图像通常用于快速响应。然而，其在洪水情境中的实用性受到云层覆盖和夜间限制等问题的阻碍，使得准确评估损害变得困难。已提出多种利用合成孔径雷达（SAR）数据的替代洪水检测技术。尽管在所述情境中SAR相比EO具有优势，但SAR呈现出一个明显缺点：人类分析人员常难解译数据。为解决此问题，本文引入一个新颖框架，即基于扩散的SAR到EO图像翻译（DSE）。DSE框架将SAR图像转换为EO图像，从而增强人类对洪水洞察的可解释性。在Sen1Floods11和SEN12-FLOOD数据集上的实验结果证实，DSE框架不仅提供了增强的视觉信息，而且提升了所有测试洪水分割基线的性能。