Hidden Markov models and morphological neural networks for GPR-based landmine detection

摘要

Previous results with Hidden Markov models showed that they could be used to perform rel;iable classification between mines and background;/clutter under a variety of conditions. Since then, new features have been defined and continuous models have been implemented. In this paper, neew 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 simultaeously. Generalizing these networks to incorporate Choquet Integrals provides the added capability of robustness and improved feature learning. These features can provide complementary information copmpared to those generated by humans. Results 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.