29 August 2016 Hyperspectral imagery classification based on probabilistic classification vector machines
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100332C (2016) https://doi.org/10.1117/12.2245012
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
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.
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Zhixiang Xue, Zhixiang Xue, Xuchu Yu, Xuchu Yu, Qiongying Fu, Qiongying Fu, Xiangpo Wei, Xiangpo Wei, Bing Liu, Bing Liu, } "Hyperspectral imagery classification based on probabilistic classification vector machines", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332C (29 August 2016); doi: 10.1117/12.2245012; https://doi.org/10.1117/12.2245012
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