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Proceedings Paper

Correlation ATR performance using Xpatch (synthetic) training data
Author(s): Abhijit Mahalanobis; Luis A. Ortiz; Bhagavatula Vijaya Kumar; Albert Ezekiel
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Paper Abstract

In this paper, we discuss the performance of correlation filter algorithms trained on Xpatch (synthetic) model images. In particular, we assess the performance of the maximum average correlation height (MACH) filter and distance classifier correlation filter (DCCF) correlation algorithms on a 3-class subset of the public release MSTAR data set. The successful performance of these algorithms on a 10-class problem has been reported in previous publications. The results reported to date however were based on filters trained on actual sensor data. The approach proposed here is viewed as a means to combine advantages of purely model-based techniques and the statistical/correlation based approaches. The paper reviews the theory of the algorithm, key practical advantages and details of test results on the 3-class public MSTAR database.

Paper Details

Date Published: 24 August 2000
PDF: 4 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396345
Show Author Affiliations
Abhijit Mahalanobis, Raytheon Systems Co. (United States)
Luis A. Ortiz, Raytheon Systems Co. (United States)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)
Albert Ezekiel, Raytheon Systems Co. (United States)

Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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