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Proceedings Paper

Statistical properties of linear correlators for image pattern classification with application to synthetic aperture radar (SAR) imagery
Author(s): Hung-Chih Chiang; Randolph L. Moses; Stanley C. Ahalt
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Paper 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.

Paper Details

Date Published: 28 March 1995
PDF: 12 pages
Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); doi: 10.1117/12.205784
Show Author Affiliations
Hung-Chih Chiang, The Ohio State Univ. (United States)
Randolph L. Moses, The Ohio State Univ. (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 2490:
Optical Pattern Recognition VI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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