Hyperspectral image classification based on Dirichlet Process mixture models

In this work, we propose a new density estimation method for hyperspectral image data based on Dirichlet Process Gaussian mixture models (also known as infinite Gaussian mixture models - IGMMs), which successfully captures the complex multi-modal (potentially non-Gaussian) statistical structure of hyperspectral data. The mixture model we get from this will then be applied to the classification problem. This IGMM based approach is a non-parametric Bayesian method helping circumvent the problem of model selection, which is unavoidable and often difficult when employing traditional parametric Gaussian mixture models (GMM). Inference model based on Gibbs sampling employed during the inference of model parameters. As a preprocessing step, we use Local Fisher´s Discriminant Analysis (LFDA) for dimension reduction since we expect it to preserve the multi-modal non-Gaussian structure of the hyperspectral data, which will benefit much in the aspect of computation cost. We compared our proposed IGMM based classification method to the existing state-of-the-art classification methods using popular hyperspectral imagery datasets. The results of our experiments show that the proposed LFDA-IGMM method and GMM method have almost the same performance (sometimes outperforming LFDA-GMM), and they outperform the other commonly used classification approaches when there is a sufficient number of training samples.