Unconditional Image Thresholding Approach of Optical and Sar Data for Large-Scale Flood Mapping

Floods are one of the most frequent and disastrous natural hazards that affect millions of people and cause damage all around the world. Satellite-based flood mapping using optical or synthetic aperture radar (SAR) data has become an important component of disaster response. For extreme event monitoring such as floods, SAR and optical data have obvious complementary advantages in terms of data availability and information richness, thereby the combined use of the two data for flood monitoring has great application potential. However, there is a lack of efficient processing methods for both SAR and optical data, which limits the cross-application of these two complementary data in flood monitoring. In this study, we develop an unconditional image thresholding approach of optical and SAR data (UIT) to monitor large-scale floods. The UIT algorithm constructs a variable Gaussian mixture model with parameter prior to express the probability distribution of water and non-water pixels, which can be effectively compatible with optical and SAR data. To reduce the omission error of the flood extent, spatial context information and dual-polarization information are exploited for fast flood detection. The experiment used Sentinel-1 SAR data and Sentinel-2 optical data to monitor two severe flooding events in the history of the Gulf of Mexico. Compared with several state-of-the-art methods, the proposed algorithm can distinguish water from non-water pixels with higher accuracy (more than 5% improvement in OA).