A Large-Scale Dataset for Water Segmentation of SAR Satellite

Not only on earth, but also in space, robot systems are increasingly becoming essential elements in our lives such as a mobile exploration robot on Mars. Satellites are also an indispensable field in space robot systems, for example, from low-orbit satellites for self-driving vehicles to small satellites launched for various purposes. A lot of research is being conducted on an automated system using more and more satellites. In particular, earth observation using satellite images is being used in various fields such as disaster prediction, damage analysis, and land cover classification. There are three main types of satellite imagery used in automation systems: optical, Synthetic Aperture Radar (SAR), and infrared. Unlike optical satellite, which is heavily influenced by weather and light, SAR satellite can acquire images in all-weather conditions. Thanks to this advantage, SAR satellite images are used in many fields, in particular, water segmentation. There are various traditional SAR image-based water segmentation methods based on thresholding technique. However, these methods are not suitable for rapidly processing a large amount of SAR images because they require a manual operation to set different thresholds for each image. In this paper, we create a large-scale dataset for water segmentation of KOrean Multi-Purpose SATellite (KOMPSAT-5) containing more than 3,000 images. 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 high performance of water segmentation can be obtained when a large number of training images are used for all five segmentation models. In addition, we confirm the possibility of the automatic water segmentation system from a large amount of SAR images, away from traditional manual work.