Hyperspectral imagery classification based on relevance vector machines

Guopeng Yang; Xuchu Yu; Wufa Feng

doi:10.1117/12.831199

30 October 2009 Hyperspectral imagery classification based on relevance vector machines

Guopeng Yang, Xuchu Yu, Wufa Feng

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960N (2009) https://doi.org/10.1117/12.831199
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

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); https://doi.org/10.1117/12.831199

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