Improved Flood Insights: Diffusion-Based SAR to EO Image Translation

TL;DR

This paper presents a diffusion-based framework that translates SAR images into EO images to improve flood assessment interpretability and performance, addressing limitations of existing flood detection methods during adverse weather conditions.

Contribution

The novel DSE framework converts SAR to EO images, enhancing flood insight interpretability and segmentation performance, a significant advancement over prior methods.

Findings

Enhanced visual quality of flood images

Improved flood segmentation accuracy

Effective on multiple datasets

Abstract

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.