Flood hazard mapping from SAR images using texture analysis and fuzzy logic

Abstract

Floods rank among the most devastating and deadly disasters likely to occur. Each year, thousands of people die as a result of rising river water levels and spring snow melts. Minimizing the impact of these events and providing necessary information to rescue teams on the ground requires the mobilization of all humanitarian stakeholders. This work aims to automate the extraction of flooded areas from Synthetic Aperture Radar (SAR) satellite images in order to accelerate the generation of damage maps during floods. Our approach is mainly based on the following steps: first, homogeneous surfaces contained in pre- and post-disaster images are extracted based on the Structural Feature Set (SFS) texture descriptor. Then, a morphological opening operator is applied to the resulting images in order to filter out small-sized objects and noise. Finally, the two gray levels images generated by applying the texture descriptor and the morphological operator are fused using fuzzy logic to identify flooded regions. SAR images acquired using the RADARSAT-2 satellite during the Richelieu River floods are used to evaluate the performance of the proposed technique, and the results obtained show the efficiency and robustness of the described approach.