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Proceedings Paper

Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data
Author(s): J. Dula; A. Zare; Dominic Ho; P. Gader
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Paper Abstract

A possibilistic K-Nearest Neighbors classifier is presented to classify mine and non-mine objects using data collected from a wideband electromagnetic induction (WEMI) sensor. The proposed classifier is motivated by the observation that buried objects often have consistent signatures depending on their metal content, size, shape, and depth. Given a joint orthogonal matching pursuits (JOMP) sparse representation, particular target types consistently selected the same dictionary elements. The proposed classifier distinguishes between target types using the frequency of dictionary elements selected by potential landmine alarms. Results are shown on data containing sixteen landmine types and several non-mine examples.

Paper Details

Date Published: 7 June 2013
PDF: 12 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87091F (7 June 2013); doi: 10.1117/12.2016490
Show Author Affiliations
J. Dula, Univ. of Missouri-Columbia (United States)
A. Zare, Univ. of Missouri-Columbia (United States)
Dominic Ho, Univ. of Missouri (United States)
P. Gader, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 8709:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII
J. Thomas Broach; Jason C. Isaacs, Editor(s)

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