Adaptive water delineation algorithms for L- and C-band SAR imagery: a comparative analysis

Abstract

Water classification in Synthetic Aperture Radar (SAR) images is an ongoing area of research, which has implications in environmental monitoring and water resource management. Adaptive threshold algorithms provide a fast, reliable and efficient way to perform automated water classification, but users often lack awareness on selecting the best algorithm for their specific application. This paper presents a comprehensive assessment of adaptive threshold algorithms for water delineation applied to L- and C-band SAR backscatter images. We introduce a novel approach for dynamic selection of windows within a SAR image to determine optimum thresholds on sigma naught values. A comparison of five threshold-determination techniques is performed which include Otsu, Kittler and Illingworth (KI), Gaussian Mixture Model (GMM), Quality Index (QI) and Gamma Maximum Likelihood Estimation (GMLE) algorithms. We observed that, for L-band SAR data, convex hull approach produced better kappa coefficient value with GMM, KI and GMLE algorithms. However, for C-band SAR, kappa coefficients were highest for convex hull method with GMM, KI, QI and GMLE approaches and noticeably higher (> 0.89) when compared to split window approach. Our analysis indicates that the proposed convex hull method for window selection performs better in both L- and C-band SAR images. The results of our analysis will help users in identifying the best adaptive algorithm for water delineation in L- and C-band SAR images.