A NOVEL UNSUPERVISED CLASSIFICATION APPROACH FOR HYPERSPECTRAL IMAGERY BASED ON SPECTRAL MIXTURE MODEL AND MARKOV RANDOM FIELD

Unsupervised classification of hyperspectral imagery (HSI) relies on a data generative model, based on which the labels of pixels and the model parameters are iteratively estimated. Traditionally, the generative model is based on the Gaussian mixture model (GMM) that describes the data generation process from a statistical perspective. However, considering the fact that a semantic class is always dominated by a particular endmember, classifying the spectral pixels based on the associated endmember-abundance pattern as described by the spectral mixture model (SMM) is more meaningful from a physical perspective. In this paper, we explore the potential of spectral mixture model for assisting unsupervised classification of HSI based on a recently proposed K-P-Means unmixing algorithm. Moreover, we investigate modeling the spatial information using Markov random field in this new context. We incorporate SMM and MRF into the Bayesiam framework and solve it via the maximum a posterior (MAP) approach. The results on both simulated and real hyperspectral images demonstrate that this new approach can effectively exploit the spatial-spectral information of HSI for improved unsupervised classification of HSI.