Texture classification of aerial image based on PCA-NBC

Abstract

Bayesian Networks have emerged in recent years as a powerful data mining technique for handling uncertainty in complex domains. The Bayesian Network represents the joint probability distribution and domain (or expert) knowledge in a compact way and provides a comprehensive method of representing relationships and influences among nodes (variables) with a graphical diagram. Actually, however, in the classification domain it was not paid attention to by researchers until the simplest of form of Bayesian Networks, Naive Bayes Classifier, turned up. Naive Bayes Classifier is a simple and efficient probability classification method, and has shown surprising performance in some domains, which owes to the independence assumption that makes Naive Bayes Classifier fit the classification more easily. However, the independence assumption obviously does not hold in the real world. Therefore, in order to meet the naive (unreal) assumption, this paper proposes a new image texture classification method of aerial images, PCA-NBC, which combines the Principal Components Analysis (PCA) and Naive Bayes Classifier (NBC). The PCA transforms the highly correlated features into statistically independent and orthogonal features, so it is suitable to solve that problem and can lay a solid theoretic foundation in the application. One hundred and thirteen aerial images are used to evaluate the classification performance in the experiment. The experimental results demonstrate that the proposed method can cut down the number of features and computational costs and improve the accuracy during classification. In one word, the new method, PCA-NBC, is an attractive and effective method, which outperforms the Naive Bayes Classifier.