Review of Segmentation Methods for Coastline Detection in SAR Images

Abstract

Synthetic aperture radar (SAR) images acquired by airborne sensors or remote sensing satellites contain the necessary information that can be used to investigate various objects of interest on the surface of the Earth, including coastlines. The coastal zone is of great economic importance and is also very densely populated. The intensive and increasing use of coasts and changes of coastlines motivate researchers to try to assess the pace of these changes. As remote sensing develops, coastlines are detected using various image processing and analysis methods, including segmentation methods. Segmentation is to allow separating water and land areas in order to obtain contours representing the shorelines of coasts. Its result has direct impact on the accuracy of the obtained contours and is one of the most important steps in image processing. This article presents an overview of state-of-the-art segmentation methods used for detecting and extracting coastlines from SAR images, taking into account the evaluation metrics used in them. Segmentation methods can be divided into three main groups: thresholding methods, active contours, and machine learning approaches. This article presents the theoretical and practical properties of individual groups of segmentation methods, their advantages and disadvantages, and also promising research directions. This article is intended to give researchers insight into existing approaches and to help them propose new, better solutions.