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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)

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.

Keywords

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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References

  1. 1.
    Gurwicz, Y., Lerner, B.: Bayesian class-matched multinet classifier. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006. LNCS, vol. 4109, pp. 145–153. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2001)zbMATHGoogle Scholar
  3. 3.
    Castillo, C., Gutiérrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models. Springer, New York (1997)Google Scholar
  4. 4.
    Cooper, G.F., Herskovitz, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)zbMATHGoogle Scholar
  5. 5.
    Cheng, J., Bell, D.A., Liu, W.: An algorithm for Bayesian belief network construction from data. In: Proc. AI & STAT 1997, pp. 83–90 (1997)Google Scholar
  6. 6.
    Cheng, J., Greiner, R.: Learning Bayesian belief network classifiers: Algorithms and system. In: Proc. 14th Canadian Conf. on Artificial Intelligence, pp. 141–151 (2001)Google Scholar
  7. 7.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)zbMATHCrossRefGoogle Scholar
  8. 8.
    Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory 14(3), 462–467 (1968)zbMATHCrossRefGoogle Scholar
  9. 9.
    Keogh, E.J., Pazzani, M.J.: Learning the structure of augmented Bayesian classifiers. Int. J. on Artificial Ingelligence Tools 11(4), 587–601 (2002)CrossRefGoogle Scholar
  10. 10.
    Murphy, K.P.: The Bayes Net Toolbox for matlab. Computing Science and Statistics 33 (2001)Google Scholar
  11. 11.
    Leray, P., Francois, O.: BNT, Structure learning package: documentation and experiments. Technical Report. Laboratoire PSI-INSA Rouen-FRE CNRS 2645 (2004)Google Scholar
  12. 12.
    Solares, C., Sanz, A.M.: Bayesian network classifiers. Some engineering applications. In: Proc. 9th IASTED Int. Conf. Artificial Intelligence and Soft Computing, pp. 331–335 (2005)Google Scholar
  13. 13.
    Ouyang, Y., Ma, J., Dai, Q.: Bayesian multinet classifier for classification of remote sensing data. Int. J. of Remote Sensing 27, 4943–4961 (2006)CrossRefGoogle Scholar

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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