Multistage classification of multispectral Earth observational data: The design approach

An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure

A multispectral data simulation technique

There are no author-identified significant results in this report

A Binary Tree Feature Selection Technique for Limited Training Sample Size

An algorithm is presented that predicts the mean recognition accuracy as a function of dimensionality for two class problems, using a Bayes classifier in the presence of a limited number of training samples. Several experiments are presented to assess the algorithm\u27s performance, and a binary tree classification procedure that utilizes the algorithm is shown to prove its usefulness