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 journalArticlepeer-review

7 Scopus citations


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 languageEnglish (US)
Pages (from-to)629-633
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number4
StatePublished - Oct 2007

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


Dive into the research topics of 'A bivariate gaussian model for unexploded ordnance classification with EMI data'. Together they form a unique fingerprint.

Cite this