Comparison of pattern recognition approaches for multisensor detection and discrimination of anti-personnel and anti-tank landmines

Peter Torrione; Jeremiah Remus; Leslie Collins

doi:10.1117/12.665660

18 May 2006 Comparison of pattern recognition approaches for multisensor detection and discrimination of anti-personnel and anti-tank landmines

Peter Torrione, Jeremiah Remus, Leslie Collins

Author Affiliations +

Proceedings Volume 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI; 62172S (2006) https://doi.org/10.1117/12.665660
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Peter Torrione, Jeremiah Remus, and Leslie Collins "Comparison of pattern recognition approaches for multisensor detection and discrimination of anti-personnel and anti-tank landmines", Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62172S (18 May 2006); https://doi.org/10.1117/12.665660

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