Relevance Vector Machine Classification of Hyperspectral Data Based on Principal Component Analysis and Linear Discriminant Analysis

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Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic. Compared to support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM. However, the classification accuracy of RVM is not high when apply to hyperspectral data. A novel classification method based on RVM is presented in this paper. The method combine principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce the dimensionality of hyperspectral data. Firstly, PCA is used to the first dimension reduction and obtain nonsingular intra-class scatter matrix. Secondly, LDA is applied to the second dimension reduction and reduce the amounts of computation great. Finally, the relevance vector machine model is applied to remote sensing image classification. The hyperspectral data of 1992 Indian Pines has been used in this paper. The experimental results show that the proposed method not only improves overall accuracy than RVM and PCA-RVM, but also extend the ratio between inter-class distances and intra-class distances.