Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite

Yang, B.; Yu, X.

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. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Bayesian Networks Augmented Naive Bayes (BAN) to texture classification of High-resolution satellite images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. In the experiment, we choose GeoEye-1 satellite images. Experimental results demonstrate BAN outperform than NBC in the overall classification accuracy. Although it is time consuming, it will be an attractive and effective method in the future.

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Yang, B. / Yu, X.: Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite. 2014. Copernicus Publications.

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Rechteinhaber: B. Yang

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