Texture classification of aerial image based on Bayesian networks

Abstract

Classification is a basic topic in data mining and pattern recognition. Following advances in computer science, a lot of new methods have been proposed in recent years, such as artificial neural networks, decision trees, fuzzy set and Bayesian Networks, etc. As a probabilistic network, Bayesian Networks is a powerful tool for handling uncertainty in data mining and many other domains. Naïve Bayes Classifier (NBC) is a simple and effective classification method, which is built on the assumption of conditional independence between the class attributes. This topology structure can not describe the inherent relation among the features. In this paper, we apply Bayesian Networks Augmented Naïve Bayes (BAN) for the texture classification of aerial images, which relaxes the independent assumption in NBC. A new method for learning the networks topology structure based on training samples is adopted in this paper. Comparison experiments show higher accuracy of BAN classifier than NBC. The results also show the potential applicability of the proposed method.