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. Separating sea surface and land areas in synthetic aperture radar (SAR) images is challenging yet of great importance to coastline extraction and subsequent coastal classification. Results of the previous state-of-art methods often suffer from a number of limitations that arise from the presence of the speckle effect and the inadequate returned signal around the boundaries. We propose a graph cut (GC)-based approach to tackle these limitations and achieve accurate sea–land segmentation results. To be more specific, as the first step, three powerful multipolarization features are extracted from the polarimetric SAR data as descriptors to fully characterize the sea area and land area. Starting from that, seeds of the sea and land are selected automatically to build the prior model for GC. Based on the prior model, we construct the undirected graph in GC using the multipolarization descriptors. Finally, we incorporate the ratio of average operator to eliminate the speckle effect and get finer results for some finer structures. Experiments on Radarsat-2 quad-polarization images demonstrate significantly improved results of our proposed algorithms compared with several state-of-the-art methods in terms of both quantitative and visual performance.

Accurate sea–land segmentation using ratio of average constrained graph cut for polarimetric synthetic aperture radar data