Different Bayesian Network Models in the Classification of Remote Sensing Images

  • Cristina Solares
  • Ana Maria Sanz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


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.


Bayesian Network Hyperspectral Image Hyperspectral Data Bayesian Belief Network Bayesian Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Cristina Solares
    • 1
  • Ana Maria Sanz
    • 1
  1. 1.University of Castilla-La ManchaSpain

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