Empirical Bayes classification rules for minefield detection

Ishwar V. Basawa

doi:10.1117/12.211351

20 June 1995 Empirical Bayes classification rules for minefield detection

Ishwar V. Basawa

Proceedings Volume 2496, Detection Technologies for Mines and Minelike Targets; (1995) https://doi.org/10.1117/12.211351
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Ishwar V. Basawa "Empirical Bayes classification rules for minefield detection", Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); https://doi.org/10.1117/12.211351

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