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Proceedings Paper

Impact of SAR image quality on recognition
Author(s): Daniel W. Carlson; Lee J. Montagnino; Robert T. Frankot
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Paper Abstract

Automatic target recognition (ATR) performance is a function of image quality and its representation in the signature model generation and used in the ATR training process. This paper reports ATR performance as a function of synthetic aperture radar (SAR) image quality parameters including clutter-to-noise ratio (CNR) and multiplicative noise ratio (MNR). Images with specified image quality values were produced by introducing controlled degradations to the MSTAR public release data. Two different families of ATR algorithms, the statistical model-based classifier of DeVore, et al., and optimal tradeoff synthetic discriminant function (OTSDF) are applied to those data. Target classification accuracy was measured as a function of CNR/MNR for both the training and test data, indicating sensitivity of performance to a priori knowledge of these particular image quality parameters. Confusion matrices are expanded to include target aspect bins, providing visibility into performance as a function of aspect angle.

Paper Details

Date Published: 19 May 2005
PDF: 11 pages
Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); doi: 10.1117/12.602431
Show Author Affiliations
Daniel W. Carlson, Raytheon Co. (United States)
Lee J. Montagnino, Raytheon Co. (United States)
Robert T. Frankot, Raytheon Co. (United States)

Published in SPIE Proceedings Vol. 5808:
Algorithms for Synthetic Aperture Radar Imagery XII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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