Detection of Explosive Hazards Using Spectrum Features From Forward-Looking Ground Penetrating Radar Imagery

Abstract

Buried explosives have proven to be a challenging problem for which ground penetrating radar (GPR) has shown to be effective. This paper discusses an explosive hazard detection algorithm for forward looking GPR (FLGPR). The proposed algorithm uses the fast Fourier transform (FFT) to obtain spectral features of anomalies in the FLGPR imagery. Results show that the spectral characteristics of explosive hazards differ from that of background clutter and are useful for rejecting false alarms (FAs). A genetic algorithm (GA) is developed in order to select a subset of spectral features to produce a more generalized classifier. Furthermore, a GA-based K-Nearest Neighbor probability density estimator is employed in which targets and false alarms are used as training data to produce a two-class classifier. The experimental results of this paper use data collected by the US Army and show the effectiveness of spectrum based features in the detection of explosive hazards.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA580950

Entities

People

  • David C. Wong
  • James M. Keller
  • Justin Farrell
  • K. C. Ho
  • Mehrdad Soumekh
  • Timothy C Havens
  • Tuan T. Ton

Organizations

  • United States Army Communications-Electronics Command

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Chromosomes
  • Classification
  • Computations
  • Data Sets
  • Detection
  • Detectors
  • Explosives
  • False Alarms
  • Feature Selection
  • Genetic Algorithms
  • Ground Penetrating Radar
  • Machine Learning
  • Radar Images
  • Training
  • Two Dimensional
  • Warning Systems

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.
  • Military/Explosive Ordnance Disposal (EOD) Technology

Technology Areas

  • AI & ML
  • Biotechnology