Water Body Detection using Deep Learning with Sentinel-1 SAR satellite data and Land Cover Maps

This paper suggests a novel and reliable method to detect water body from Sentinel-1 SAR satellite data using deep learning technique. There have been a lot of studies to extract water body from SAR images with deep learning. Although they achieved good performance, most of them used training data without guaranteeing good quality. In this study, land cover map generated by an official government agency were used for labelling ground truth data. After identifying the acquisition date of aerial photo used for generating the land cover map, vector polygons for river or reservoir were extracted and used as label data. This new method reduced producing time and cost to generate reliable training data. After training our deep learning model, it showed 0.874 of f1score. We also tested our deep learning model to the heavy rain season in Korea (August 2020) and successfully detected river flooding.