A bivariate gaussian model for unexploded ordnance classification with EMI data

Published

Journal Article

A bivariate Gaussian model is proposed for modeling spatially varying electromagnetic-induction (EMI) response of unexploded ordnance (UXO). This model is proposed for EMI sensors that do not exploit enough physics to warrant using the popular magnetic-dipole model currently commonly used. These two competing models are applied to measured EM61 sensor data at a real UXO site. UXO classification performance using the proposed bivariate Gaussian model is shown to be superior to an approach employing the magnetic-dipole model. Moreover, the bivariate Gaussian model requires no labeled training data, obviates classifier construction, and has fewer model parameters to learn. © 2007 IEEE.

Full Text

Duke Authors

Cited Authors

  • Williams, D; Yu, Y; Kennedy, L; Zhu, X; Carin, L

Published Date

  • October 1, 2007

Published In

Volume / Issue

  • 4 / 4

Start / End Page

  • 629 - 633

International Standard Serial Number (ISSN)

  • 1545-598X

Digital Object Identifier (DOI)

  • 10.1109/LGRS.2007.903972

Citation Source

  • Scopus