  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.
