Water/land segmentation for sar images based on geodesic distance

In this paper, a novel method for water/land segmentation is proposed based on the framework of geodesic distance. The proposed method models the water/land according to the statistics of both the speckle and land covers, which leads to a fast point-wised coarse segmentation. Based on the water/land models, the boundary area between water and land can be localized with automatically generated class labels and adaptively determined bandwidth. Then the refined segmentation is implemented using an improved geodesic distance, combining the manifold idea to enlarge the inter-class differences. Experimental results on real synthetic aperture radar (SAR) images demonstrate the effectiveness and efficiency of the method. The bridges, harbors and coastline can be segmented correctly with very tiny details preserved.