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30 October 2009 Hyperspectral imagery classification based on relevance vector machines
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960N (2009)
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guopeng Yang, Xuchu Yu, and Wufa Feng "Hyperspectral imagery classification based on relevance vector machines", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960N (30 October 2009);

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