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Proceedings Paper

Probabilistic neural networks for the discrimination of subsurface unexploded ordnance (UXO) in magnetometry surveys
Author(s): Sean J. Hart; Ronald E. Shaffer; Susan L. Rose-Pehrsson; James R. McDonald
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Paper 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.

Paper Details

Date Published: 23 November 1999
PDF: 9 pages
Proc. SPIE 3856, Internal Standardization and Calibration Architectures for Chemical Sensors, (23 November 1999); doi: 10.1117/12.371292
Show Author Affiliations
Sean J. Hart, Nova Research, Inc. (United States)
Ronald E. Shaffer, Naval Research Lab. (United States)
Susan L. Rose-Pehrsson, Naval Research Lab. (United States)
James R. McDonald, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 3856:
Internal Standardization and Calibration Architectures for Chemical Sensors
Ronald E. Shaffer; Radislav A. Potyrailo, Editor(s)

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