Probabilistic neural networks for the discrimination of subsurface unexploded ordnance (UXO) in magnetometry surveys

Sean J. Hart; Ronald E. Shaffer; Susan L. Rose-Pehrsson; James R. McDonald

doi:10.1117/12.371292

23 November 1999 Probabilistic neural networks for the discrimination of subsurface unexploded ordnance (UXO) in magnetometry surveys

Sean J. Hart, Ronald E. Shaffer, Susan L. Rose-Pehrsson, James R. McDonald

Author Affiliations +

Proceedings Volume 3856, Internal Standardization and Calibration Architectures for Chemical Sensors; (1999) https://doi.org/10.1117/12.371292
Event: Photonics East '99, 1999, Boston, MA, United States

Abstract

The outputs from a physics-based modeler of magnetometry data have been successfully used with a probabilistic neural network (PNN) to discriminate UXO from scrap. Model outputs from one location at a site were used to train a PNN model, which could correctly discriminate UXO from scrap at a different location of the same site. Data from one site location, the Badlands Bombing Range, Bull's Eye 2 (BBR 2), was used to predict targets detected at a different location at the site, Badlands Bombing Range, Bull's Eye 1 (BBR 1) containing different types of items. The UXO detection rate obtained for this analysis was 93 percent with a false alarm rate of only 28 percent. The possibility of discriminant individual UXO types within the context of a coarser two- class problem was demonstrated. The utility of weighting the sum of squared errors in cross-validation optimization of the (sigma) parameter has been demonstrated as a method of improving the classification of UXO versus scrap.

Citation Download Citation

Sean J. Hart, Ronald E. Shaffer, Susan L. Rose-Pehrsson, and James R. McDonald "Probabilistic neural networks for the discrimination of subsurface unexploded ordnance (UXO) in magnetometry surveys", Proc. SPIE 3856, Internal Standardization and Calibration Architectures for Chemical Sensors, (23 November 1999); https://doi.org/10.1117/12.371292

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