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23 June 1997 Extraction of features for classification of impulse radar measurements
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Abstract
The present paper addresses the problem of extracting features for classification purposes. A vector valued sample is to be classified to one of a number of classes with known distributions using the Bayes decision rule. The complexity of the classifier depends on the dimension of the vectors; thus it is of interest to keep this dimension as small as possible. One way to reduce the dimension is to apply a linear transformation on data. This transformation should be chosen so that no 'essential' information is lost. There are several suggestions on how this concept should be defined. We study a measure of class separability defined as the mean of all interclass Mahalanobis distances. The method to be presented, however, applies to all weighted quadratic distance measures. The validity of the proposed transformation is justified by applying the transformation to both Monte Carlo simulated data and to actual measured data. The measured data come from an impulse radar system with the purpose of classifying buried objects. The proposed transformation is shown to outperform the well known principal component analysis (PCA).
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hakan O. Brunzell "Extraction of features for classification of impulse radar measurements", Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); https://doi.org/10.1117/12.277119
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