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Proceedings Paper

Combinative hypergraph learning on oil spill training dataset
Author(s): Binghui Wei; Ming Cheng; Cheng Wang; Jonathan Li
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Paper Abstract

Detecting oil spill from open sea based on Synthetic Aperture Radar (SAR) image is a very important work. One of key issues is to distinguish oil spill from “look-alike”. There are many existing methods to tackle this issue including supervised and semi-supervised learning. Recent years have witnessed a surge of interest in hypergraph-based transductive classification. This paper proposes combinative hypergraph learning (CHL) to distinguish oil spill from “look-alike”. CHL captures the similarity between two samples in the same category by adding sparse hypergraph learning to conventional hypergraph learning. Experimental results have demonstrated the effectiveness of CHL in comparison to the state-of-the-art methods and showed that our proposed method is promising.

Paper Details

Date Published: 2 March 2016
PDF: 9 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010Z (2 March 2016); doi: 10.1117/12.2234854
Show Author Affiliations
Binghui Wei, Xiamen Univ. (China)
Jiangxi Univ. of Science and Technology (China)
Ming Cheng, Xiamen Univ. (China)
Cheng Wang, Xiamen Univ. (China)
Jonathan Li, Xiamen Univ. (China)
Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)

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