Gaussian mixture classifier with regularized covariance estimator for hyperspectral data classification

New Gaussian mixture classifiers are designed by replacing the maximum likelihood covariance estimator with regularized covariance estimator in both parameters estimation and model selection steps. The results of simulated and real data experiments suggested that nearest mean clustering and Bayesian information criterion with regularized covariance estimator is a better choice to build a Gaussian mixture classifier.