Discriminant analysis using certain normed exponential densities with emphasis on remote sensing application

摘要

The Bayes discriminant analysis based upon the normality assumption for population models does not lead to an exact evaluation of probabilities of correct classification and of misclassification unless it is restricted to a simplest possible situation. In order to overcome this and other computational difficulties that one faces in a complex situation such as the remote sensing, certain alternative densities are posed as models for the observations. It is shown that for a Bayes discriminant analysis these densities lead to piecewise linear discriminant functions even when the covariance matrices are unequal (a property not enjoyed in the normal case) and provide a theoretical solution for evaluating probabilities of correct classification and of misclassification. Also, some computational advantages are achieved.