A GAN-Based SAR-Optical Data Fusion Approach for Enhanced Flood Mapping

Accurate and prompt flood mapping plays a crucial role in disaster response and damage assessment. Synthetic Aperture Radar (SAR) satellites are commonly employed for this task due to their ability to capture images regardless of weather conditions, day or night. However, SAR images suffer from speckle noise and optical sensors gain semantic information that SAR sensors cannot capture. In this study, we enhanced SAR flood mapping accuracy by incorporating optical information. First, we utilized a public SAR-optical paired dataset of the flood mapping area to train image translation models that convert SAR images into an optical style. Then, we trained flood segmentation models using both the original and the translated SAR data. We confirmed that a generative adversarial network translated images with high quality, and that segmentation models trained with a combination of SAR and optical data performed better than those trained with single-domain data.