An improved frequency domain feature with partial least-squares dimensionality reduction for classifying buried threats in forwardlooking ground-penetrating radar data

Joseph A. Camilo, Miles Crosskey, Kenneth Morton, Leslie M. Collins, Jordan M. Malof

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

Abstract

Forward-looking ground penetrating radar (FLGPR) is a remote sensing modality that has been investigated for buried threat detection. The FLGPR considered in this work consists of a sensor array mounted on the front of a vehicle, which inspects an area in front of the vehicle as it moves down a lane. The FLGPR collects data using a stepped frequency approach, and the received radar data is processed by filtered backprojection to create images of the subsurface. A large body of research has focused on developing effective supervised machine learning algorithms to automatically discriminate between imagery associated with target and non-target FLGPR responses. An important component of these automated algorithms is the design of effective features (e.g., image descriptors) that are extracted from the FLGPR imagery and then provided to the machine learning classifiers (e.g., support vector machines). One feature that has recently been proposed is computed from the magnitude of the two-dimensional fast Fourier transform (2DFFT) of the FLGPR imagery. This paper presents a modified version of the 2DFFT feature, termed 2DFFT+, that yields substantial detection performance when compared with several other existing features on a large collection of FLGPR imagery. Further, we show that using partial least-squares discriminative dimensionality reduction, it is possible to dramatically lower the dimensionality of the 2DFFT+ feature from 2652 dimensions down to twenty dimensions (on average), while simultaneously improving its performance.

Original languageEnglish
Title of host publicationDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
EditorsSteven S. Bishop, Jason C. Isaacs
PublisherSPIE
ISBN (Electronic)9781510608658
DOIs
StatePublished - 2017
EventDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII 2017 - Anaheim, United States
Duration: Apr 10 2017Apr 12 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10182
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII 2017
Country/TerritoryUnited States
CityAnaheim
Period04/10/1704/12/17

Keywords

  • FLGPR
  • Feature reduction
  • Forward-looking
  • Frequency feature
  • GPR
  • Radar imagery

Fingerprint

Dive into the research topics of 'An improved frequency domain feature with partial least-squares dimensionality reduction for classifying buried threats in forwardlooking ground-penetrating radar data'. Together they form a unique fingerprint.

Cite this