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

Hung-Chih Chiang; Randolph L. Moses; Stanley C. Ahalt

doi:10.1117/12.205784

28 March 1995 Statistical properties of linear correlators for image pattern classification with application to synthetic aperture radar (SAR) imagery

Hung-Chih Chiang, Randolph L. Moses, Stanley C. Ahalt

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Proceedings Volume 2490, Optical Pattern Recognition VI; (1995) https://doi.org/10.1117/12.205784
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

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.

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Hung-Chih Chiang, Randolph L. Moses, and Stanley C. Ahalt "Statistical properties of linear correlators for image pattern classification with application to synthetic aperture radar (SAR) imagery", Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); https://doi.org/10.1117/12.205784

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KEYWORDS

Optical correlators

Synthetic aperture radar

Linear filtering

Image filtering

Detection and tracking algorithms

Signal to noise ratio

Image classification

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