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Proceedings Paper

Comparison of pattern recognition approaches for multisensor detection and discrimination of anti-personnel and anti-tank landmines
Author(s): Peter Torrione; Jeremiah Remus; Leslie Collins
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Paper Abstract

In this work we explore and compare several statistical pattern recognition techniques for classification and identification of buried landmines using both electromagnetic induction and ground penetrating radar data. In particular we explore application of different feature extraction approaches to the problem of landmine/clutter classification in blind- and known- ground truth scenarios using data from the NIITEK ground penetrating radar and the Vallon EMI sensor as well as the CyTerra GPR and Minelab EMI sensors. We also compare and contrast the generalization capabilities of different kernels including radial basis function, linear, and direct kernels within the relevance vector machine framework. Results are presented for blind-test scenarios that illustrate robust classification for features that can be extracted with low computational complexity.

Paper Details

Date Published: 18 May 2006
PDF: 11 pages
Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62172S (18 May 2006); doi: 10.1117/12.665660
Show Author Affiliations
Peter Torrione, Duke Univ. (United States)
Jeremiah Remus, Duke Univ. (United States)
Leslie Collins, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 6217:
Detection and Remediation Technologies for Mines and Minelike Targets XI
J. Thomas Broach; Russell S. Harmon; John H. Holloway, Editor(s)

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