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Proceedings Paper

Side attack mine detection using near infra-red imagery
Author(s): John McElroy; Chris Hawkins; Paul D. Gader; James M. Keller; Robert Luke
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Paper Abstract

Near Infra-Red (NIR) offers enhanced contrast of man-made objects against vegetation. Shape detection algorithms for identifying side-attack mines in sequences of NIR imagery are described. These algorithms use morphological representations of features of the object in a network that learns features and classification simultaneously. A training set was constructed using NIR images of side attack mines. Testing sets were constructed using pairs of sequences of NIR images. Each pair of sequences contains a sequence containing a side attack mine and another sequence of the same scene with no side attack mine. Testing results from these sequences are presented.

Paper Details

Date Published: 10 June 2005
PDF: 12 pages
Proc. SPIE 5794, Detection and Remediation Technologies for Mines and Minelike Targets X, (10 June 2005); doi: 10.1117/12.604190
Show Author Affiliations
John McElroy, Univ. of Florida (United States)
Chris Hawkins, Univ. of Florida (United States)
Paul D. Gader, Univ. of Florida (United States)
James M. Keller, Univ. of Missouri/Columbia (United States)
Robert Luke, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 5794:
Detection and Remediation Technologies for Mines and Minelike Targets X
Russell S. Harmon; J. Thomas Broach; John H. Holloway, Editor(s)

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