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Proceedings Paper

Quadratic distance classifier for multiclass SAR ATR using correlation filters
Author(s): Abhijit Mahalanobis; Arthur V. Forman; Mark Roger Bower; Nathalie Day; Rich F Cherry
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Paper Abstract

The recognition of targets in synthetic aperture radar (SAR) imagery using a quadratic classifier is proposed. Correlators are used to compute distances under an optimum transform to measure similarity between ideal reference images and the actual data. The transform is a filter which responds to features specifically useful for discrimination. This is attractive for model based training since only a similarity in features is required between the actual images and their class models rather than a precise match in pixel values. The quadratic terms are unaffected by shifting of the input image while linear terms are computed using shift-invariant correlation. The system is thus non-linear but shift-invariant. Specifically, 'distance' vectors are generated by the filter banks which are analyzed by a rudimentary rule base to determine whether the input is a target image or clutter. In this paper, we describe a SAR automatic target recognizer (ATR) with results for 3 and 5 class problems. the data used is actual SAR imagery of military targets.

Paper Details

Date Published: 28 May 1993
PDF: 12 pages
Proc. SPIE 1875, Ultrahigh Resolution Radar, (28 May 1993); doi: 10.1117/12.145517
Show Author Affiliations
Abhijit Mahalanobis, Martin Marietta Electronic Systems (United States)
Arthur V. Forman, Martin Marietta Electronic Systems (United States)
Mark Roger Bower, Martin Marietta Electronic Systems (United States)
Nathalie Day, Martin Marietta Electronic Systems (United States)
Rich F Cherry, Martin Marietta Electronic Systems (United States)

Published in SPIE Proceedings Vol. 1875:
Ultrahigh Resolution Radar
Roger S. Vickers, Editor(s)

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