Investigation of computational vision and principal component analysis with application to target classification

Abstract

A comparison between metrics based on computational vision (CV) and principal component analysis (PCA) is been performed. A CV metric is developed based on the response of the CAMAELEON model and compared with a PCA metric on the basis of synthetic aperture radar (SAR) target chip classification. The two techniques are not correlated and are, to some degree, independent. The independence of these techniques could be used to enhance the decision process in aided target recognition (ATR) applications. In addition, it appears that the use of PCA gives a simple way to detect the presence of a target. The evidence indicates that the method used here could be used as a sophisticated window filter to find regions of interest in images.