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Proceedings Paper

Hyperspectral imagery classification based on probabilistic classification vector machines
Author(s): Zhixiang Xue; Xuchu Yu; Qiongying Fu; Xiangpo Wei; Bing Liu
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Paper Abstract

Though the support vector machine and relevance vector machine have been successfully applied in hyperspectral imagery classification, they also have several limitations. In this paper, a hyperspectral imagery classification method based on the probabilistic classification vector machines is proposed. In the Bayesian framework, a signed and truncated Gaussian prior is adopted over every weight in the probabilistic classification vector machines, where the sign of prior is determined by the class label, and the EM algorithm has been adopted for the parametric inference to generate a sparse model. This algorithm can solve the problem that the relevance vector machine is based on some untrustful vectors, which influences the accuracy and stability of the model. The experiments on the OMIS and PHI images were performed, and the results show the advantages of the hyperspectral imagery classification method based on probabilistic classification vector machines.

Paper Details

Date Published: 29 August 2016
PDF: 7 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332C (29 August 2016); doi: 10.1117/12.2245012
Show Author Affiliations
Zhixiang Xue, Information Engineering Univ. (China)
Xuchu Yu, Information Engineering Univ. (China)
Qiongying Fu, Information Engineering Univ. (China)
Xiangpo Wei, Information Engineering Univ. (China)
Bing Liu, Information Engineering Univ. (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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