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28 October 2006 Texture classification of aerial image using Bayesian networks
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Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64191E (2006)
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. Recently Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. However, in the classification domain it was not paid attention to by researchers until the simplest form of Bayesian Networks, Naive Bayesian Network, turned up. In this paper, Naive Bayesian Network is applied to texture classification of aerial image. In order to validate the utility of Naive Bayesian Classifier, six hundred and eighty-four aerial images are used in the experiment and results demonstrate Naive Bayesian Classifier needs less computational costs than maximum likelihood method during classification and outperforms maximum likelihood method in the classification accuracy. Therefore, it is an attractive and effective method, and it will lead to its wide application.
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Xin Yu, Zhaobao Zheng, Linyi Li, and Lei Cai "Texture classification of aerial image using Bayesian networks", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191E (28 October 2006);

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