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Proceedings Paper

Context-dependent feature selection for landmine detection with ground-penetrating radar
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Paper Abstract

We present a novel method for improving landmine detection with ground-penetrating radar (GPR) by utilizing a priori knowledge of environmental conditions to facilitate algorithm training. The goal of Context-Dependent Feature Selection (CDFS) is to mitigate performance degradation caused by environmental factors. CDFS operates on GPR data by first identifying its environmental context, and then fuses the decisions of several classifiers trained on context-dependent subsets of features. CDFS was evaluated on GPR data collected at several distinct sites under a variety of weather conditions. Results show that using prior environmental knowledge in this fashion has the potential to improve landmine detection.

Paper Details

Date Published: 5 May 2009
PDF: 12 pages
Proc. SPIE 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV, 730327 (5 May 2009); doi: 10.1117/12.817946
Show Author Affiliations
Christopher R. Ratto, Duke Univ. (United States)
Peter A. Torrione, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 7303:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV
Russell S. Harmon; J. Thomas Broach; John H. Holloway, Editor(s)

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