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Proceedings Paper

MSE template size analysis for MSTAR data
Author(s): Michael Lee Bryant; Steven W. Worrell; Anson C. Dixon
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Paper Abstract

Analysis of statistical pattern recognition algorithms is typically performed using stationary, gaussian noise to simplify the analysis. An example is the excellent paper titled, `Effects of Sample Size in Classifier Design', which was written by Keinosuke Fukunaga and Raymond Hayes and published in the August 1989 issue of IEEE Transactions on Pattern Analysis and Machine Intelligence. One of the main conclusions of this paper is that more training samples will improve the estimation of classifier design parameters and classifier performance. This conclusion is valid when the observed signatures are stationary. However, when the observed signatures are non-stationary, as is the case for the synthetic aperture radar data collected for the Moving and Stationary Target Acquisition and Recognition program, more samples can actually corrupt the design parameter estimation process and lead to degraded performance. This fact has been known for some time, which explains the standard practice of designing templates at various pose angles. However, no theory currently exists to determine the optimum number of signatures to use in the template design process. This paper presents some initial work to determine the optimum number of samples to use.

Paper Details

Date Published: 15 September 1998
PDF: 10 pages
Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); doi: 10.1117/12.321844
Show Author Affiliations
Michael Lee Bryant, Air Force Research Lab. (United States)
Steven W. Worrell, Air Force Research Lab. (United States)
Anson C. Dixon, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 3370:
Algorithms for Synthetic Aperture Radar Imagery V
Edmund G. Zelnio, Editor(s)

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