Analysis and modification of linear correlators for image pattern classification

The paper considers linear correlation filters used for image pattern recognition. First, the authors develop a statistical theory to predict the classification performance of a general class of correlation filters for wide sense stationary (WSS) clutter. This analysis includes as special cases the synthetic discriminant function (SDF), the minimum variance SDF (MVSDF), and the minimum average correlation energy (MACE) filters. Second, the authors develop a modified filter design applicable to nonzero mean noise; this latter case occurs in many applications where the magnitude image is used for classification. They compare the performance of several filters on synthetic radar imagery