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

Kejiang Wu; Xiaojian Xu

doi:10.1117/12.2503531

9 October 2018 Sea-land segmentation in SAR images based on multifeature fused boundary clustering

Kejiang Wu, Xiaojian Xu

Proceedings Volume 10794, Target and Background Signatures IV; 107940T (2018) https://doi.org/10.1117/12.2503531
Event: SPIE Security + Defence, 2018, Berlin, Germany

Abstract

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.

Citation Download Citation

Kejiang Wu and Xiaojian Xu "Sea-land segmentation in SAR images based on multifeature fused boundary clustering", Proc. SPIE 10794, Target and Background Signatures IV, 107940T (9 October 2018); https://doi.org/10.1117/12.2503531

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