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Proceedings Paper

Landmine detection using discrete hidden Markov models with Gabor features
Author(s): Hichem Frigui; Oualid Missaoui; Paul Gader
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Paper Abstract

We propose a general method for detecting landmine signatures in vehicle mounted ground penetrating radar (GPR) using discrete hidden Markov models and Gabor wavelet features. Observation vectors are constructed based on the expansion of the signature's B-scan using a bank of scale and orientation selective Gabor filters. This expansion provides localized frequency description that gets encoded in the observation sequence. These observations do not impose an explicit structure on the mine model, and are used to naturally model the time-varying signatures produced by the interaction of the GPR and the landmines as the vehicle moves. The proposed method is evaluated on real data collected by a GPR mounted on a moving vehicle at three different geographical locations that include several lanes. The model parameters are optimized using the BaumWelch algorithm, and lane-based cross-validation, in which each mine lane is in turn treated as a test set with the rest of the lanes used for training, is used to train and test the model. Preliminary results show that observations encoded with Gabor wavelet features perform better than observation encoded with gradient-based edge features.

Paper Details

Date Published: 7 May 2007
PDF: 10 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65532A (7 May 2007); doi: 10.1117/12.722241
Show Author Affiliations
Hichem Frigui, Univ. of Louisville (United States)
Oualid Missaoui, Univ. of Louisville (United States)
Paul Gader, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)

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