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Proceedings Paper

Mine detection in multispectral imagery data using constrained energy minimization
Author(s): M. I. Elbakary; M. S. Alam
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Paper Abstract

Multispectral imagery is used for a wide variety of military and commercial applications, including object detection such as mines. The main reason for using multispectral imagery is that it reveals spectral information about the scene which cannot be obtained from a single spectral band. This paper introduces a new algorithm for mine detection in multispecral imagery using the constrained energy minimization (CEM) approach. The CEM approach is introduced as classifier. The novelty of this idea is that this classifier uses only the information of the mines for training and enabling the potential mines without using information about the clutter in the scene. Using only mines information for detection is a major advantage of the CEM approach. In addition, the CEM approach is modified such that recomputing the autocorrelation matrix is not necessary and using the algorithm became scene independent Then, to reduce the false alarm further, morphological processing and stochastic expectation maximization (SEM) algorithm are employed for post-processing. The results of the proposed algorithm were promising when the algorithm is tested using real multispectral imagery.

Paper Details

Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6977, Optical Pattern Recognition XIX, 697707 (17 March 2008); doi: 10.1117/12.781735
Show Author Affiliations
M. I. Elbakary, Univ. of South Alabama (United States)
M. S. Alam, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 6977:
Optical Pattern Recognition XIX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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