Separation Between Water and Land in SAR Images Using Region-Based Level Sets

Abstract

This letter presents a method for the separation between land and water in synthetic aperture radar (SAR) amplitude images. The proposed technique uses region-based level sets and adopts a mixture of lognormal densities as the probabilistic model for the pixel intensities in both water and land classes. The expectation-maximization algorithm is used to estimate the probability density functions for each class. Experimental results with real SAR images of riverbeds, flood extent areas, and shorelines demonstrate the good performance of the proposed algorithm compared with state-of-the-art approaches.