Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

Darren Shaw; Kevin Stone; K. C. Ho; James M. Keller; Robert H. Luke; Brian P. Burns

doi:10.1117/12.2224272

3 May 2016 Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

Darren Shaw, Kevin Stone, K. C. Ho, James M. Keller, Robert H. Luke, Brian P. Burns

Proceedings Volume 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI; 98231L (2016) https://doi.org/10.1117/12.2224272
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Forward looking ground penetrating radar (FLGPR) has the benefit of detecting objects at a significant standoff distance. The FLGPR signal is radiated over a large surface area and the radar signal return is often weak. Improving detection, especially for buried in road targets, while maintaining an acceptable false alarm rate remains to be a challenging task. Various kinds of features have been developed over the years to increase the FLGPR detection performance. This paper focuses on investigating the use of as many features as possible for detecting buried targets and uses the sequential feature selection technique to automatically choose the features that contribute most for improving performance. Experimental results using data collected at a government test site are presented.

Citation Download Citation

Darren Shaw, Kevin Stone, K. C. Ho, James M. Keller, Robert H. Luke, and Brian P. Burns "Sequential feature selection for detecting buried objects using forward looking ground penetrating radar", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231L (3 May 2016); https://doi.org/10.1117/12.2224272

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