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

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.