How much shape information is enough, or too much? Designing imaging descriptors for threat detection in ground penetrating radar data

Published

Conference Paper

© 2018 SPIE. In this work, we consider the development of algorithms for automated buried threat detection (BTD) using Ground Penetrating Radar (GPR) data. When viewed in GPR imagery, buried threats often exhibit hyperbolic shapes, and this characteristic shape can be leveraged for buried threat detection. Consequentially, many modern detectors initiate processing the received data by extracting visual descriptors of the GPR data (i.e., features). Ideally, these descriptors succinctly encode all decision-relevant information, such as shape, while suppressing spurious data content (e.g., random noise). Some notable examples of successful descriptors include the histogram of oriented gradient (HOG), and the edge histogram descriptor (EHD). A key difference between many descriptors is the precision with which shape information is encoded. For example, HOG encodes shape variations over both space and time (high precision); while EHD primarily encodes shape variations only over space (lower precision). In this work, we conduct experiments on a large GPR dataset that suggest EHD-like descriptors outperform HOG-like descriptors, as well as exhibiting several other practical advantages. These results suggest that higher resolution shape information (particularly shape variations over time) is not beneficial for buried threat detection. Subsequent analysis also indicates that the performance advantage of EHD is most pronounced among difficult buried threats, which also exhibit more irregular shape patterns.

Full Text

Duke Authors

Cited Authors

  • Reichman, D; Collins, LM; Malof, JM

Published Date

  • January 1, 2018

Published In

Volume / Issue

  • 10628 /

Electronic International Standard Serial Number (EISSN)

  • 1996-756X

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 13 (ISBN-13)

  • 9781510617674

Digital Object Identifier (DOI)

  • 10.1117/12.2305880

Citation Source

  • Scopus