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Proceedings Paper

Hyperspectral imagery classification based on relevance vector machines
Author(s): Guopeng Yang; Xuchu Yu; Wufa Feng
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Paper Abstract

The relevance vector machine is sparse model in the Bayesian framework, its mathematics model doesn't have regularization coefficient and its kernel functions don't need to satisfy Mercer's condition. RVM present the good generalization performance, and its predictions are probabilistic. In this paper, a hyperspectral imagery classification method based on the relevance machine is brought forward. We introduce the sparse Bayesian classification model, regard the RVM learning as the maximization of marginal likelihood, and select the fast sequential sparse Bayesian learning algorithm. Through the experiment of PHI imagery classification, the advantages of the relevance machine used in hyperspectral imagery classification are given out.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960N (30 October 2009); doi: 10.1117/12.831199
Show Author Affiliations
Guopeng Yang, Zhengzhou Institute of Surveying and Mapping (China)
Xuchu Yu, Zhengzhou Institute of Surveying and Mapping (China)
Wufa Feng, Zhengzhou Institute of Surveying and Mapping (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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