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Feature analysis for forward-looking landmine detection using GPR

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.