Parameter estimation for bayesian classification of multispectral data

Abstract

In this paper, we present two algorithms for estimating the parameters of a Bayes classifier for remote sensing multispectral data. The first algorithm uses the Support Vector Machines (SVM) as a multi-dimensional density estimator. This algorithm is a supervised one in the sense that it needs in advance, the specification of the number of classes and a training sample for each class. The second algorithm employs the Expectation Maximization (EM) algorithm, in an unsupervised way, for estimating the number of classes and the parameters of each class in the data set. Performance comparison of the presented algorithms shows that the SVM- based classifier outperforms those based on Gaussian-based and Parzen window algorithms. We also show that the EM based classifier provides comparable results to Gaussian-based and Parzen window-based while it is an unsupervised algorithm.