
Proceedings Paper
A feature learning approach for classifying buried threats in forward looking ground penetrating radar dataFormat | Member Price | Non-Member Price |
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Paper 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.
Paper Details
Date Published: 3 May 2016
PDF: 10 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231I (3 May 2016); doi: 10.1117/12.2223117
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)
PDF: 10 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231I (3 May 2016); doi: 10.1117/12.2223117
Show Author Affiliations
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)
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