Water-Body Segmentation for SAR Images: Past, Current, and Future

Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.

Automated summary

This paper reviews the literature on water body extraction in SAR images over the past 30 years and proposes some suggestions for the SAR image waterbody extraction community. It summarizes the main ideas and characteristics of traditional water body extraction methods, especially traditional Machine Learning (ML) methods. It also summarizes the application and optimization of Deep Learning (DL) methods in water-body segmentation for SAR images at the pixel and image levels, with a focus on popular networks such as U-Net and novel networks such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. The paper concludes with an in-depth discussion of the limitations and challenges of DL for water-body segmentation and provides future trends.