How transferable are downward-looking and handheld ground penetrating radar data? Experiments in the context of buried threat detection

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 using handheld Ground Penetrating Radar (HH-GPR) data. The development of algorithms for HH-GPR is relatively nascent compared to algorithm development efforts for larger downward-looking GPR (DL-GPR) systems. One of the biggest bottlenecks for the development of algorithms is the relative scarcity of labeled HH-GPR data that can be used for development. Given the similarities between DL-GPR data and HH-GPR data however, we hypothesized that it may be possible to utilize DL-GPR data to support the development of algorithms for HH-GPR. In this work we assess the detection performance of a HH-GPR-based BTD algorithm as we vary the amounts and characteristics of the DL-GPR data included in the development of HH-GPR detection algorithms. The results indicate that supplementing HH-GPR data with DL-GPR does improve performance, especially when including data collected over buried threat locations.

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

  • Transfer learning
  • algorithm
  • buried threat detection
  • feature extraction
  • ground penetrating radar
  • handheld

Fingerprint

Dive into the research topics of 'How transferable are downward-looking and handheld ground penetrating radar data? Experiments in the context of buried threat detection'. Together they form a unique fingerprint.

Cite this