An improved Expectation Maximization algorithm for hyperspectral image classification

Abstract

In this paper, we propose an improved Expectation Maximization (EM) algorithm for hyperspectral image classification. As an excellent machine learning algorithm, EM is an iterative process for finding Maximum A Posteriori estimation (MAP) of parameters in Gaussian Mixture Models (GMMs). With the ability to deal with missing data, EM is considered excellent for solving the insufficient samples training problem of hyperspectral data classification. In the new algorithm, specially aimed at highly mixing hyperspectral data, endmember class separability metric is added into the convergence criteria of improved EM, which may yield better classification result than traditional EM. Three classification algorithms based on statistical probability were tested: the maximum likelihood method (ML), traditional EM, and improved EM. Experimental results on simulated data and real hyperspectral image demonstrate that improved EM can get higher classification accuracy in the case of a small number of training samples.