Ground penetrating radar (GPR) has the ability to detect buried targets with little or no metal content. Achieving superior detection performance with a hand-held GPR can be very challenging due to the quality of the data, inconsistency of target signatures, variety of target types, and effects of a human operator. In this paper, we investigate the use of a local binary patterns (LBP) feature vector for target versus non-target discrimination from hand-held GPR data. First, a prescreener algorithm is applied to the GPR data. Then, a GPR B-scan is gathered at each prescreener alarm location and separated into several spatial and depth regions. LBP processing is applied to each spatial and depth cell individually and the LBP features from each cell are group together to form a feature vector. The resulting LBP features are invariant to amplitude scaling and represent the texture in the data. Using this feature vector, a classifier is trained to perform target versus non-target discrimination at each prescreener declaration. Experimental results illustrate the ability of the LBP features to improve detection of buried targets, especially low-metal and non-metal anti-tank and anti-personnel targets.

Date Published: 3 May 2017
PDF: 9 pages
Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820Z (3 May 2017); doi: 10.1117/12.2263199

Published in SPIE Proceedings Vol. 10182:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
Steven S. Bishop; Jason C. Isaacs, Editor(s)