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Proceedings Paper

Multiple instance learning for landmine detection using ground penetrating radar
Author(s): Achut Manandhar; Kenneth D. Morton; Leslie M. Collins; Peter A. Torrione
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Paper Abstract

Ground Penetrating Radar (GPR) has been extensively employed as a technology for the detection of subsurface buried threats. Although vehicular mounted GPRs generate data in three dimensions, alarm declarations are usually only available in the form of 2-D spatial coordinates. The uncertainty in the depth of the target in the three dimensional volume of data, and the difficulties associated with automatically localizing objects in depth, can adversely impact feature extraction and training in some detection algorithms. In order to mitigate the negative impact of uncertainty in target depth, several algorithms have been developed that extract features from multiple depth regions and utilize these feature vectors in classification algorithms to perform final mine/nonmine decisions. However, the uncertainty in object depth significantly complicates learning since features at the correct target depth are often significantly different from features at other depths but in the same volume. Multiple Instance Learning (MIL) is a type of supervised learning approach in which labels are available for a collection of feature vectors but not for individual samples, or in this application, depths. The goal of MIL is to classify new collections of vectors as they become available. This set-based learning method is applicable in the landmine detection problem because features that are extracted independently from several depth bins can be viewed as a set of unlabeled feature vectors, where the entire set either corresponds to a buried threat or a false alarm. In this work, a novel generative Dirichlet Process Gaussian mixture model for MIL is developed that automatically infers the number of mixture components required to model the underlying distributions of mine/non-mine signatures and performs classification using a likelihood ratio test. In this work, we show that the performance of the proposed approach for discriminating targets from non-targets in GPR data is promising.

Paper Details

Date Published: 11 May 2012
PDF: 11 pages
Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 835721 (11 May 2012); doi: 10.1117/12.917947
Show Author Affiliations
Achut Manandhar, Duke Univ. (United States)
Kenneth D. Morton, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)
Peter A. Torrione, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 8357:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII
J. Thomas Broach; John H. Holloway, Editor(s)

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