Comparison of bootstrap and prior-probability synthetic data balancing methods for SAR target recognition

Erik P. Blasch; Stephen G. Alsing; Kenneth W. Bauer Jr.

doi:10.1117/12.357689

13 August 1999 Comparison of bootstrap and prior-probability synthetic data balancing methods for SAR target recognition

Erik P. Blasch, Stephen G. Alsing, Kenneth W. Bauer Jr.

Author Affiliations +

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357689
Event: AeroSense '99, 1999, Orlando, FL, United States

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.

Citation Download Citation

Erik P. Blasch, Stephen G. Alsing, and Kenneth W. Bauer Jr. "Comparison of bootstrap and prior-probability synthetic data balancing methods for SAR target recognition", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357689

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