Computational Intelligence Methods in Forward-Looking Explosive Hazard Detection

  • Timothy C. Havens
  • Derek T. Anderson
  • Kevin Stone
  • John Becker
  • Anthony J. Pinar
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 621)

Abstract

This chapter discusses several methods for forward-looking (FL) explosive hazard detection (EHD) using FL infrared (FLIR) and FL ground penetrating radar (FLGPR). The challenge in detecting explosive hazards with FL sensors is that there are multiple types of targets buried at different depths in a highly-cluttered environment. A wide array of target and clutter signatures exist, which makes detection algorithm design difficult. Recent work in this application has focused on fusion methods, including fusion of multiple modalities of sensors (e.g., GPR and IR), fusion of multiple frequency sub-band images in FLGPR, and feature-level fusion using multiple kernel and iECO learning. For this chapter, we will demonstrate several types of EHD techniques, including kernel methods such as support vector machines (SVMs), multiple kernel learning MKL, and feature learning methods, including deep learners and iECO learning. We demonstrate the performance of several algorithms using FLGPR and FLIR data collected at a US Army test site. The summary of this work is that deep belief networks and evolutionary approaches to feature learning were shown to be very effective both for FLGPR and FLIR based EHD.

Keywords

Sensor fusion Explosive hazard detection Aggregation Multiple kernel learning Deep learning Fuzzy integral 

Notes

Acknowledgments

This work is funded in part by a National Institute of Justice grant (2011-DN-BX-K838), U.S. Army (W909MY-13-C0013, W909MY-13-C0029) and Army Research Office (W911NF-14-1-0114 and 57940-EV) in support of the U.S. Army RDECOM CERDEC NVESD. Superior, a high performance computing cluster at Michigan Technological University, was used in obtaining results presented in this work.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Timothy C. Havens
    • 1
  • Derek T. Anderson
    • 2
  • Kevin Stone
    • 3
  • John Becker
    • 1
  • Anthony J. Pinar
    • 1
  1. 1.Department of Electrical and Computer Engineering/Department of Computer ScienceMichigan Technological UniversityHoughtonUSA
  2. 2.Department of Electrical and Computer EngineeringMississippi State UniversityMississippi StateUSA
  3. 3.Department of Electrical and Computer EngineeringUniversity of MissouriColumbiaUSA

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