Dynamic mixing kernels in Gaussian mixture classifier for hyperspectral classification

In this paper, new Gaussian mixture classifiers are designed to deal with the case of an unknown number of mixing kernels. Not knowing the true number of mixing components is a major learning problem for a mixture classifier using expectation-maximization (EM). To overcome this problem, the training algorithm uses a combination of covariance constraints, dynamic pruning, splitting and merging of mixture kernels of the Gaussian mixture to correctly automate the learning process. This structural learning of Gaussian mixtures is employed to model and classify Hyperspectral imagery (HSI) data. The results from the HSI experiments suggested that this new methodology is a potential alternative to the traditional mixture based modeling and classification using general EM.