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Proceedings Paper

MINACE filter classification algorithms for ATR using MSTAR data
Author(s): Rohit Patnaik; David Casasent
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Paper Abstract

A synthetic aperture radar (SAR) automatic target recognition (ATR) system based on the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF) is presented. A set of MINACE filters covering different aspect ranges is synthesized for each object using a training set of images of that object and a validation set of confuser and clutter images. No prior DIF work addressed confuser rejection. We also address use of fewer DIFs per object than prior work did. The selection of the MINACE filter parameter c for each filter is automated using training and validation sets. The system is evaluated using images from the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database. The classification scores (PC) and the number of false alarm scores for confusers and clutter (PFA and PCFA respectively) are presented for the benchmark three-class MSTAR database with object variants and two confusers. The pose of the input test image is not assumed to be known, thus the problem addressed is more realistic than in prior work, since pose estimation of SAR objects has a large margin of error. Results for both confuser and clutter rejection are presented.

Paper Details

Date Published: 19 May 2005
PDF: 12 pages
Proc. SPIE 5807, Automatic Target Recognition XV, (19 May 2005); doi: 10.1117/12.603065
Show Author Affiliations
Rohit Patnaik, Carnegie Mellon Univ. (United States)
David Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 5807:
Automatic Target Recognition XV
Firooz A. Sadjadi, Editor(s)

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