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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2001)
Castillo, C., Gutiérrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models. Springer, New York (1997)
Cooper, G.F., Herskovitz, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
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)
Cheng, J., Greiner, R.: Learning Bayesian belief network classifiers: Algorithms and system. In: Proc. 14th Canadian Conf. on Artificial Intelligence, pp. 141–151 (2001)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory 14(3), 462–467 (1968)
Keogh, E.J., Pazzani, M.J.: Learning the structure of augmented Bayesian classifiers. Int. J. on Artificial Ingelligence Tools 11(4), 587–601 (2002)
Murphy, K.P.: The Bayes Net Toolbox for matlab. Computing Science and Statistics 33 (2001)
Leray, P., Francois, O.: BNT, Structure learning package: documentation and experiments. Technical Report. Laboratoire PSI-INSA Rouen-FRE CNRS 2645 (2004)
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)
Ouyang, Y., Ma, J., Dai, Q.: Bayesian multinet classifier for classification of remote sensing data. Int. J. of Remote Sensing 27, 4943–4961 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Solares, C., Sanz, A.M. (2007). Different Bayesian Network Models in the Classification of Remote Sensing Images. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)