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Proceedings Paper

Convolutional neural network approach for buried target recognition in FL-LWIR imagery
Author(s): K. Stone; J. M. Keller
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Paper Abstract

A convolutional neural network (CNN) approach to recognition of buried explosive hazards in forward-looking long-wave infrared (FL-LWIR) imagery is presented. The convolutional filters in the first layer of the network are learned in the frequency domain, making enforcement of zero-phase and zero-dc response characteristics much easier. The spatial domain representations of the filters are forced to have unit l2 norm, and penalty terms are added to the online gradient descent update to encourage orthonormality among the convolutional filters, as well smooth first and second order derivatives in the spatial domain. The impact of these modifications on the generalization performance of the CNN model is investigated. The CNN approach is compared to a second recognition algorithm utilizing shearlet and log-gabor decomposition of the image coupled with cell-structured feature extraction and support vector machine classification. Results are presented for multiple FL-LWIR data sets recently collected from US Army test sites. These data sets include vehicle position information allowing accurate transformation between image and world coordinates and realistic evaluation of detection and false alarm rates.

Paper Details

Date Published: 29 May 2014
PDF: 15 pages
Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 907219 (29 May 2014); doi: 10.1117/12.2049958
Show Author Affiliations
K. Stone, Univ. of Missouri-Columbia (United States)
J. M. Keller, Univ. of Missouri-Columbia (United States)


Published in SPIE Proceedings Vol. 9072:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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