An Algorithm for Classification of Multi-Spectral Data and its Implementation on a Massively Parallel Computer

摘要

A new method for classification of multi-spectral data is proposed. This method is based on fitting mixtures of multivariate Gaussian components to training and unlabeled samples by using the EM algorithm. Through a backtracking search strategy with appropriate depth bounds, a series of mixture models are compared. The validity of the candidate models are evaluated by considering their description lengths and allocation rates. The most suitable model is selected and the multi-spectral data are classified accordingly. The EM algorithm is mapped onto a massively parallel computer system to reduce the computational cost. Experimental results show that the proposed algorithm is more robust against variations in training samples than the conventional supervised Gaussian maximum likelihood classifier.