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Sea-land segmentation in SAR images based on multifeature fused boundary clustering
Author(s): Kejiang Wu; Xiaojian Xu
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Paper 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.

Paper Details

Date Published: 9 October 2018
PDF: 8 pages
Proc. SPIE 10794, Target and Background Signatures IV, 107940T (9 October 2018); doi: 10.1117/12.2503531
Show Author Affiliations
Kejiang Wu, Beihang Univ. (China)
Xiaojian Xu, Beihang Univ. (China)

Published in SPIE Proceedings Vol. 10794:
Target and Background Signatures IV
Karin U. Stein; Ric Schleijpen, Editor(s)

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