Active learning schemes for reduced dimensionality hyperspectral classification

Statistical schemes have certain advantages which promote their use in various pattern recognition problems. In this paper, we study the application of two statistical learning criteria for material classification of Hyperspectral remote sensing data. In most cases, the Hyperspectral data is characterized using a Gaussian mixture model (GMM). The problem in using statistical model such as the GMM is the estimation of class conditional probability density functions based on the exemplar available from the training data for each class. We demonstrate the usage of two training methods - dynamic component allocation (DCA) and the minimum message length (MML) criteria that are employed to learn the mixture observations. The training schemes are then evaluated using the Bayesian classifier.