Share Email Print
cover

Proceedings Paper

Algorithm development for deeply buried threat detection in GPR data
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Ground penetrating radar (GPR) is a popular remote sensing modality for buried threat detection. Many algorithms have been developed to detect buried threats using GPR data. One on-going challenge with GPR is the detection of very deeply buried targets. In this work a detection approach is proposed that improves the detection of very deeply buried targets, and interestingly, shallow targets as well. First, it is shown that the signal of a target (the target “signature”) is well localized in time, and well correlated with the target’s burial depth. This motivates the proposed approach, where GPR data is split into two disjoint subsets: an early and late portion corresponding to the time at which shallow and deep target signatures appear, respectively. Experiments are conducted on real GPR data using the previously published histogram of oriented gradients (HOG) prescreener: a fast supervised processing method operated on HOG features. The results show substantial improvements in detection of very deeply buried targets (4.1% to 17.2%) and in overall detection performance (81.1% to 83.9%). Further, it is shown that the performance of the proposed approach is relatively insensitive to the time at which the data is split. These results suggest that other detection methods may benefit from depth-based processing as well.

Paper Details

Date Published: 3 May 2016
PDF: 8 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231A (3 May 2016); doi: 10.1117/12.2222593
Show Author Affiliations
Daniël Reichman, Duke Univ. (United States)
Jordan M. Malof, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 9823:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Steven S. Bishop; Jason C. Isaacs, Editor(s)

© SPIE. Terms of Use
Back to Top