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Proceedings Paper

Hidden Markov models and morphological neural networks for GPR-based land mine detection
Author(s): Paul D. Gader; Ali Koksal Hocaoglu; Miroslaw Mystkowski; Y. Zhao
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Paper Abstract

Previous results with Hidden Markov models showed that they could be used to perform reliable classification between mines and background/clutter under a variety of conditions. Since the, new features have been defined and continuous models have been implemented. In this paper, new results are presented for applying them to calibration lane GPR data obtained during the vehicle mounted mine detection (VMMD) Advanced Technology Demonstrations. Morphological Neural Networks can be trained to perform feature extraction and detection simultaneously. Generalizing these networks to incorporate Choquet Integrals provides the added capability of robustness and improved feature learning. These features can provide complementary information compared to those generate by humans. Result of applying these networks to calibration lane GPR data from the VMMD Advanced Technology Demonstrations are provided. Combinations of the various methodologies with previously developed algorithms are also evaluated.

Paper Details

Date Published: 22 August 2000
PDF: 12 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396195
Show Author Affiliations
Paul D. Gader, Univ. of Missouri/Columbia (United States)
Ali Koksal Hocaoglu, Univ. of Missouri/Columbia (United States)
Miroslaw Mystkowski, Univ. of Missouri/Columbia (United States)
Y. Zhao, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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