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Proceedings Paper

Comparison of bootstrap and prior-probability synthetic data balancing methods for SAR target recognition
Author(s): Erik P. Blasch; Stephen G. Alsing; Kenneth W. Bauer
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Paper Abstract

This paper compares bootstrap techniques with prior probability synthetic data balancing to determine which method is more effective for SAR target recognition. A bootstrap method resamples from the original target data to present more target examples to the ATR for training. Prior probability synthetic data balancing prevents the double counting of information by just resampling the smaller set. However, prior probability synthetic data balancing necessitates equivalent distributions from data sets which reduces the data set to the size of the smaller set. A new type of receiver operating characteristic (ROC) curve, based on varying the proportion of target data in the data set is presented to compare the two methods. The paper demonstrates the implementation of the data balancing of two targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set using an entropy metric for target classification.

Paper Details

Date Published: 13 August 1999
PDF: 8 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357689
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
Stephen G. Alsing, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)


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

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