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Proceedings Paper

Combined evolutionary algorithm and minimum classification error training for DHMM based land mine detection
Author(s): Yunxin Zhao; Ping Chen; Paul D. Gader; Yue Zhang
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Paper Abstract

Minimum classification error (MCE) training is proposed to improve performance of a discrete hidden Markov model (DHMM) based landmine detection system. The system (baseline) was proposed previously for detection of both metal and nonmetal mines from ground penetrating radar signatures collected by moving vehicles. An initial DHMM model is trained by conventional methods of vector quantization and Baum-Welch algorithm. A sequential generalized probabilistic descent (GPD) algorithm that minimizes an empirical loss function is then used to estimate the landmine/background DHMM parameters, and an evolutionary algorithm based on fitness score of classification accuracy is used to generate and select codebooks. The landmine data of one geographical site was used for model training, and those of two different sites were used for evaluation of system performance. Three scenarios were studied: apply MCE/GPD alone to DHMM estimation, apply EA alone to codebook generation, first apply EA to codebook generation and then apply MCE/GPD to DHMM estimation. Overall, the combined EA and MCE/GPD training led to the best performance. At the same level of detection rate as the baseline DHMM system, the proposed training reduced false alarm rate by a factor of two, indicating significant performance improvement.

Paper Details

Date Published: 13 August 2002
PDF: 12 pages
Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); doi: 10.1117/12.479077
Show Author Affiliations
Yunxin Zhao, Univ. of Missouri/Columbia (United States)
Ping Chen, Univ. of Missouri/Columbia (United States)
Paul D. Gader, Univ. of Florida (United States)
Yue Zhang, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 4742:
Detection and Remediation Technologies for Mines and Minelike Targets VII
J. Thomas Broach; Russell S Harmon; Gerald J. Dobeck, Editor(s)

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