Different Bayesian network models in the classification of remote sensing images

Authors:
Cristina Solares;Ana Maria Sanz
Affiliations:
University of Castilla-La Mancha, Spain;University of Castilla-La Mancha, Spain
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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Abstract

In this paper we study the application of Bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of Bayesian networks as: Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and General Bayesian Networks (GBN), are applied to the classification of hyperspectral data. In addition, several Bayesian multi-net models: TAN multi-net, GBN multi-net and the model developed by Gurwicz and Lerner, TAN-Based Bayesian Class-Matched multi-net (tBCM2) (see [1]) are applied to the classification of multispectral data. A comparison of the results obtained with the different classifiers is done.