The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have bean receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

© 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This paper appears in the IEEE Transactions on Geoscience and Remote Sensing Vol. 37, No. 4, July 1999. This paper describes a covariance estimator formulated under an empirical Bayesian setting to mitigate the problem of limited training samples in the Gaussian maximum likelihood classification for remote sensing. The most suitable covariance mixture is selected by maximizing the average leave-one-out log likelihood. Experimental results using AVIRIS data are presented. Index Terms: Gaussian Maximum Likelihood, regularization, covariance estimation.

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Hyperspectral data potentially contain more information than multispectral data because of higher dimensionality. Information extraction algorithm performance is strongly related to the quantitative precision with which the desired classes are defined, a characteristic which increases rapidly with dimensionality. Due to the limited number of training samples used in defining classes, the information extraction of hyperspectral data may not perform as well as needed. In this paper, schemes for statistics enhancement are investigated for alleviating this problem. Previous works including the EM algorithm and the Leave-One-Out covariance estimator are discussed. The HALF covariance estimator is proposed for two-class problems by using the symmetry property of the normal distribution. A spectral-spatial labeling scheme is proposed to increase the training sample sizes automatically. We also seek to combine previous works with the proposed methods so as to take full advantage of statistics enhancement. Using these techniques, improvement in classification accuracy has been observed.

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The aim of this work is to select a set of features, which have good performance to solve the problem of signature recognition of different sizes. The signature database was formed for three sizes of signatures per user, small, median and big. This study used structural features, pseudo-dynamic features and five moment groups. The feature selection method chosen was the one that select the best individual features based on classifiers like bayes and k-NN.

© 1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Presented at the 1998 International Geoscience and