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

摘要

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. !20