Abstract
Abstract. 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.
Highlights
Image classification will still be a long way in the future, it has gone almost half a century
Some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect
This paper puts up a new method, to construct the topology structure of Bayesian Network Augmented Naive Bayes (BAN), and it can resolve the forenamed problem, because it allows arbitrary relation among features, which can be obtained in terms of training accuracy based on training data
Summary
Image classification will still be a long way in the future, it has gone almost half a century. Some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. This paper will apply a new method in the artificial intelligence domain----Bayesian networks (Friedman, N., 1997), to image classification domain. By virtue of advantages of Bayesian networks we will try our best to explore a new road to texture classification of High-resolution satellite images for achieving the automatization and intelligentization of photogrammetry and remote sensing. This paper puts up a new method, to construct the topology structure of Bayesian Network Augmented Naive Bayes (BAN), and it can resolve the forenamed problem (or assumption), because it allows arbitrary relation (arc) among features, which can be obtained in terms of training accuracy based on training data (samples).
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