Statistical properties of linear correlators for image pattern classification with application to synthetic aperture radar (SAR) imagery

In this paper we consider linear correlation filters for image pattern recognition, with particular application to Synthetic Aperture Radar (SAR). We investigate the statistical properties of several popular Synthetic Discriminate Function (SDF) based linear correlation filters, including SDF, MVSDF, and MACE filters. We compare these statistical properties both qualitatively and analytically for SAR applications. We also develop modifications to these SDF-type filters which have particular utility for Synthetic Aperture Radar (SAR) image classification. We compare the performance of the modified filters to the standard filters using X-patch generated SAR images with both white and colored noise. We also investigate effects of performance degradation caused by mis-estimated noise statistics, and the effects of image normalization on the target detection rates.