Statistical properties of linear correlators for image pattern classi cation with application to SAR imagery
نویسندگان
چکیده
In this paper we consider linear correlation lters 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 lters, including SDF, MVSDF, and MACE lters. We compare these statistical properties both qualitatively and analytically for SAR applications. We also develop modiications to these SDF-type lters which have particular utility for Synthetic Aperture Radar (SAR) image classiication. We compare the performance of the modiied lters to the standard lters using X-patch generated SAR images with both white and colored noise. We also investigate eeects of performance degradation caused by mis-estimated noise statistics, and the eeects of image normalization on the target detection rates.
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