An exploration of gradient-based features for buried threat detection using a handheld ground penetrating radar

Evan Stump, Daniel Reichman, Leslie M. Collins, Jordan M. Malof

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work we consider the problem of developing algorithms for the automatic detection of buried threats in handheld Ground Penetrating Radar (HH-GPR) data. The development of algorithms for HH-GPR is relatively nascent compared to larger downward-looking GPR (DL-GPR) systems. A large number of buried threat detection (BTD) algorithms have been developed for DL-GPR systems. Given the similarities between DL-GPR data and HHGPR data, effective BTD algorithm designs may be similar for both modalities. In this work we explore the application of successful class of DL-GPR-based algorithms to HH-GPR data. In particular, we consider the class of algorithms that are based upon gradient-based features, such as histogram-of-oriented gradients (HOG) and edge histogram descriptors. We apply a generic gradient-based feature with a support vector machine to a large dataset of HH-GPR data with known buried threat locations. We measure the detection performance of the algorithm as we vary several important design parameters of the feature, and identify those designs that yield the best performance. The results suggest that the design of the gradient histogram (GH) feature has a substantial impact on its performance. We find that a tuned GH algorithm yields substantially-better performance, but ultimately performs similarly to the energy-based detector. This suggests that GH-based features may not be beneficial for HH-GPR data, or that further innovation will be needed to achieve benefits.

Original languageEnglish
Title of host publicationDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
EditorsSteven S. Bishop, Jason C. Isaacs
PublisherSPIE
ISBN (Electronic)9781510626898
DOIs
StatePublished - 2019
EventDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV 2019 - Baltimore, United States
Duration: Apr 15 2019Apr 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11012
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV 2019
Country/TerritoryUnited States
CityBaltimore
Period04/15/1904/17/19

Keywords

  • EHD
  • algorithm
  • buried threat detection
  • feature extraction
  • handheld ground penetrating radar

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