Unsupervised classification method for hyperspectral image combining PCA and Gaussian mixture model

Abstract

An unsupervised classification method combining Principal Component Analysis (PCA) and Gaussian Mixture Model for hyperspectral image is proposed in this paper. It is based on the property that lower dimensional linear projections of high dimensional data sets have the tendency to be Gaussian, or a combination of Gaussian distributions as the dimension increases. The spectral dimensionality of the data is first reduced by a PCA linear projection; then the transformed data is modeled by a Gaussian mixture models, the parameters of the model are estimated using the Expectation-Maximimization (EM) algorithm in merge operations and the number of components is automatically selected based on Bayesian Information Criterion (BIC); finally the data after PCA transform is classified according to the mixture model. Applying the method to Push-broom Hyperspectral Imager (PHI) data shows that the method is quite effective without any a prior information.