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Proceedings Paper

Live-site UXO classification studies using advanced EMI and statistical models
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Paper Abstract

In this paper we present the inversion and classification performance of the advanced EMI inversion, processing and discrimination schemes developed by our group when applied to the ESTCP Live-Site UXO Discrimination Study carried out at the former Camp Butner in North Carolina. The advanced models combine: 1) the joint diagonalization (JD) algorithm to estimate the number of potential anomalies from the measured data without inversion, 2) the ortho-normalized volume magnetic source (ONVMS) to represent targets' EMI responses and extract their intrinsic "feature vectors," and 3) the Gaussian mixture algorithm to classify buried objects as targets of interest or not starting from the extracted discrimination features. The studies are conducted using cued datasets collected with the next-generation TEMTADS and MetalMapper (MM) sensor systems. For the cued TEMTADS datasets we first estimate the data quality and the number of targets contributing to each signal using the JD technique. Once we know the number of targets we proceed to invert the data using a standard non-linear optimization technique in order to determine intrinsic parameters such as the total ONVMS for each potential target. Finally we classify the targets using a library-matching technique. The MetalMapper data are all inverted as multi-target scenarios, and the resulting intrinsic parameters are grouped using an unsupervised Gaussian mixture approach. The potential targets of interest are a 37-mm projectile, an M48 fuze, and a 105-mm projectile. During the analysis we requested the ground truth for a few selected anomalies to assist in the classification task. Our results were scored independently by the Institute for Defense Analyses, who revealed that our advanced models produce superb classification when starting from either TEMTADS or MM cued datasets.

Paper Details

Date Published: 23 May 2011
PDF: 12 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 801709 (23 May 2011); doi: 10.1117/12.884404
Show Author Affiliations
I. Shamatava, Sky Research, Inc. (United States)
Thayer School of Engineering at Dartmouth (United States)
F. Shubitidze, Thayer School of Engineering at Dartmouth (United States)
Sky Research, Inc. (United States)
J. P. Fernandez, Thayer School of Engineering at Dartmouth (United States)
A. Bijamov, Thayer School of Engineering at Dartmouth (United States)
B. E. Barrowes, Thayer School of Engineering at Dartmouth (United States)
U.S. Army Cold Regions Research and Engineering Lab. (United States)
K. O'Neill, Thayer School of Engineering at Dartmouth (United States)
U.S. Army Cold Regions Research and Engineering Lab. (United States)


Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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