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Proceedings Paper

Empirical Bayes classification rules for minefield detection
Author(s): Ishwar V. Basawa
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Paper Abstract

Emperical Bayes classification rules are derived for minefield detection. Laplace approximations for the likelihood function and for the posterior density are used to construct approximate Bayes rules for classifying each unit or region as belonging to one of the two possible types, indicating the presence or absence of mines. Approximate maximum likelihood estimation is proposed assuming that repeated observations are available. An application to a multivariate Poisson log normal model is discussed briefly.

Paper Details

Date Published: 20 June 1995
PDF: 12 pages
Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); doi: 10.1117/12.211351
Show Author Affiliations
Ishwar V. Basawa, Univ. of Georgia (United States)


Published in SPIE Proceedings Vol. 2496:
Detection Technologies for Mines and Minelike Targets
Abinash C. Dubey; Ivan Cindrich; James M. Ralston; Kelly A. Rigano, Editor(s)

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