Feature analysis for forward-looking landmine detection using GPR

Tsaipei Wang; Ozy Sjahputera; James M. Keller; Paul D. Gader

doi:10.1117/12.604185

10 June 2005 Feature analysis for forward-looking landmine detection using GPR

Tsaipei Wang, Ozy Sjahputera, James M. Keller, Paul D. Gader

Proceedings Volume 5794, Detection and Remediation Technologies for Mines and Minelike Targets X; (2005) https://doi.org/10.1117/12.604185
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

There has been significant amount of study on the use of Ground-Penetrating Radar (GPR) for forward-looking landmine detection. This paper presents our analysis of GPR data collected at a U.S. Army site using the Synthetic Aperture Radar system developed by Stanford Research Institute (SRI). Various types of features are extracted from the GPR data and investigated for their abilities to distinguish buried landmines and false alarms; the list include intensity and local-contrast features, fuzzy geometrical image features, ratio between co-polarization and cross-polarization signals, and features obtained using two different approaches of polarimetric decomposition. We also describe the feature selection procedures employed to find subsets of features that improve detection performance when combined. In addition, our analysis indicates that images formed with different frequency bands have different qualities, and that the selection of proper frequency bands can significantly improve the detection performance. Results of landmine detection, including performance on blind test lanes, are presented.

Citation Download Citation

Tsaipei Wang, Ozy Sjahputera, James M. Keller, and Paul D. Gader "Feature analysis for forward-looking landmine detection using GPR", Proc. SPIE 5794, Detection and Remediation Technologies for Mines and Minelike Targets X, (10 June 2005); https://doi.org/10.1117/12.604185

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