A bivariate gaussian model for unexploded ordnance classification with EMI data

David Williams; Yijun Yu; Levi Kennedy; Xianyang Zhu; Lawrence Carin

doi:10.1109/LGRS.2007.903972

A bivariate gaussian model for unexploded ordnance classification with EMI data

David Williams, Yijun Yu, Levi Kennedy, Xianyang Zhu, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	629-633
Number of pages	5
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	4
Issue number	4
DOIs	https://doi.org/10.1109/LGRS.2007.903972
State	Published - Oct 2007

All Science Journal Classification (ASJC) codes

Geotechnical Engineering and Engineering Geology
Electrical and Electronic Engineering

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10.1109/LGRS.2007.903972

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title = "A bivariate gaussian model for unexploded ordnance classification with EMI data",

abstract = "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.",

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AU - Zhu, Xianyang

AU - Carin, Lawrence

PY - 2007/10

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A bivariate gaussian model for unexploded ordnance classification with EMI data

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