Sea-land segmentation in SAR images based on multifeature fused boundary clustering

Sea-land segmentation in synthetic aperture radar (SAR) images is a challenge due to the high complexity of littoral environment and speckle noise. In this work, we focus on develop a new procedure for sea-land segmentation of SAR images based on multi-feature fused boundary clustering. Multi-feature fusion, which combines strong scattering and high gradient features, is adopted to achieve fragmented boundaries of the original SAR images. Multi-direction clustering combined with possible geographic information is used to distinguish the real coastlines from the fragmented boundaries. Space-borne SAR image are processed to validate the proposed method. The results demonstrate that the multi-feature fusion technique can improve the accuracy in low-scattered land discrimination and the integrality in coastline detection.