A feature learning approach for classifying buried threats in forward looking ground penetrating radar data

Joseph A. Camilo, Jordan M. Malof, Leslie M. Collins

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

Abstract

The forward-looking ground penetrating radar (FLGPR) is a remote sensing modality that has recently been investigated for buried threat detection. The FLGPR considered in this work uses stepped frequency sensing followed by filtered backprojection to create images of the ground, where each image pixel corresponds to the radar energy reflected from the subsurface at that location. Typical target detection processing begins with a prescreening operation where a small subset of spatial locations are chosen to consider for further processing. Image statistics, or features, are then extracted around each selected location and used for training a machine learning classification algorithm. A variety of features have been proposed in the literature for use in classification. Thus far, however, predominantly hand-crafted or manually designed features from the computer vision literature have been employed (e.g., HOG, Gabor filtering, etc.). Recently, it has been shown that image features learned directly from data can obtain state-of-the-art performance on a variety of problems. In this work we employ a feature learning scheme using k-means and a bag-of-visual-words model to learn effective features for target and non-target discrimination in FLGPR data. Experiments are conducted using several lanes of FLGPR data and learned features are compared with several previously proposed static features. The results suggest that learned features perform comparably, or better, than existing static features. Similar to other feature learning results, the features consist of edges or texture primitives, revealing which structures in the data are most useful for discrimination.

Original languageEnglish
Title of host publicationDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
EditorsJason C. Isaacs, Steven S. Bishop
PublisherSPIE
ISBN (Electronic)9781510600645
DOIs
StatePublished - 2016
EventDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI - Baltimore, United States
Duration: Apr 18 2016Apr 21 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9823
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Country/TerritoryUnited States
CityBaltimore
Period04/18/1604/21/16

Keywords

  • Bag-of-words
  • FLGPR
  • Feature learning
  • Forward-looking
  • Landmine detection

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