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Proceedings Paper

Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set
Author(s): Abhijit Mahalanobis; Luis A. Ortiz; Bhagavatula Vijaya Kumar
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Paper Abstract

The maximum average correlation height (MACH) filter and distance classifier correlation filter (DCCF) correlation algorithms are evaluated using the 10 class publicly released MSTAR database. The successful performance of these algorithms on a 3-class problem has been previously reported. The algorithms are optimized by design to be robust to variations (distortions) in the target's signature as well as discriminate between classes. Unlike Matched Filtering (or other template based methods), the proposed approach requires relatively few filters. The paper reviews the theory of the algorithm, key practical advantages and details of test results on the 10-class public MSTAR database.

Paper Details

Date Published: 13 August 1999
PDF: 7 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357646
Show Author Affiliations
Abhijit Mahalanobis, Raytheon Missile Systems Co. (United States)
Luis A. Ortiz, Raytheon Missile Systems Co. (United States)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 3721:
Algorithms for Synthetic Aperture Radar Imagery VI
Edmund G. Zelnio, Editor(s)

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