Algorithm for classification of multispectral data and its implementation on a massively parallel computer

Behzad M. Shahshahani; David A. Landgrebe

doi:10.1117/12.162033

29 October 1993 Algorithm for classification of multispectral data and its implementation on a massively parallel computer

Behzad M. Shahshahani, David A. Landgrebe

Author Affiliations +

Proceedings Volume 2032, Neural and Stochastic Methods in Image and Signal Processing II; (1993) https://doi.org/10.1117/12.162033
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

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.

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Behzad M. Shahshahani and David A. Landgrebe "Algorithm for classification of multispectral data and its implementation on a massively parallel computer", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); https://doi.org/10.1117/12.162033

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