Combinative hypergraph learning on oil spill training dataset

Binghui Wei; Ming Cheng; Cheng Wang; Jonathan Li

doi:10.1117/12.2234854

2 March 2016 Combinative hypergraph learning on oil spill training dataset

Binghui Wei, Ming Cheng, Cheng Wang, Jonathan Li

Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 99010Z (2016) https://doi.org/10.1117/12.2234854
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China

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.

Citation Download Citation

Binghui Wei, Ming Cheng, Cheng Wang, and Jonathan Li "Combinative hypergraph learning on oil spill training dataset", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010Z (2 March 2016); https://doi.org/10.1117/12.2234854

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available