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Proceedings Paper

Iterative nonlinear technique for automatic detection of land mines in highly cluttered multispectral images
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Paper Abstract

Automatic mine detection is an area of intense research due to the implications in humanistic and battlefield management related issues. In this paper, we describe a fully automatic and iterative implementation of the nonlinear MM-MNF algorithm and review its performance for detecting landmines in multi-spectral images provided by the Coastal Battlefield Reconnaissance and Analysis program. The MM-MNF algorithm utilizes a powerful linear multi-spectral enhancement tool, called the Maximum Noise Fraction (MNF) transform, in conjunction with a nonlinear detection device based on mathematical morphology. The iterative implementation of this algorithm improves the accuracy of the clutter covariance estimation, which is turn decreases the number of false alarms, as compared to a previously reported implementation. The result are significantly better than the ones obtained from a constant false alarm rate algorithm, known as the RX-algorithm, whose performance was also inferior to the previous implementation of the MM-MNF algorithm.

Paper Details

Date Published: 2 August 1999
PDF: 10 pages
Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); doi: 10.1117/12.357105
Show Author Affiliations
Sinan Batman, Johns Hopkins Univ. (United States)
John Ioannis Goutsias, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 3710:
Detection and Remediation Technologies for Mines and Minelike Targets IV
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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