Classification of Landsat ETM+ Data Using a Bayesian Multi-net Classifier

Abstract

The purpose of this study is to investigate the applicability of Bayesian Multi-net Classifier (BMC) to classify a Landsat ETM+ image of Beijing acquired on May 1, 2003. BMC is based on Bayesian Network (BN), which is a graphical model encoding probabilistic relationships among variables of interest. Different from the BNC that has a mere network, a BMC has as many local Bayesian Networks as the predefined classes, which means that the probabilistic relationships among the features can be different for different classes. Classification is done by computing the probability of the class given the particular instance of the features, and then predicting the class with the highest posterior probability. Based on the overall accuracy, Kappa statistic and a relative measure-total normalized probability of misclassification (TNPM), classification results of BMC were compared with those of Maximum Likelihood Classifier (MLC) and Bayesian Network Classifier (BNC) in the case study. It is concluded from the comparison results that BMC is an effective approach in this case study.