Deep Learning Based Water Segmentation Using KOMPSAT-5 SAR Images

Abstract

Depending on weather conditions, optical satellites might not acquire image information of a region of interest (ROI). This is a major drawback in emergency disaster which require realtime monitoring and damage analysis. In particular, one of the most serious disasters, floods, always accompany clouds. As a result, there are difficulties in flood detection, i.e., water detection, using optical satellite images. While, Synthetic Aperture Radar (SAR) satellite has the advantage of acquiring images regardless of weather conditions such as cloud and rain. Therefore, we can effectively perform flood monitoring and damage analysis for the ROI through water detection using SAR satellite images. In this paper, we propose a deep learning-based water segmentation using KOrean Multi-Purpose SATellite (KOMPSAT-5) images. To efficiently develop the deep learning-based model, we create a SAR water dataset for over 3,000 sheets based on KOMPSAT-5. And We perform water segmentation using representative deep learning-based segmentation models such as Fully Convolutional Networks (FCN), U-Net, DeepUNet, and High Resolution Network (HRNet). Experimental results show that HRNet performs the highest accuracy, i.e, this model achieves more than 80% IoU (Intersection over Union).