Context-dependent landmine detection with ground-penetrating radar using a hidden Markov context model

Published

Conference Paper

Context-dependent approaches to landmine detection have been developed in recent years to exploit the sensitivity of ground-penetrating radar (GPR) to changes in environmental conditions. Previous approaches to context-dependent fusion have only considered the special case of statistically independent observations. This work proposes the use of Hidden Markov Models, trained on the GPR background, for modeling the context of observation sequences. The performances of context-dependent fusion using two statistical context models were compared in an experiment with field data. One approach utilized a Hidden Markov Context Model (HMCM), and the other utilized a Gaussian mixture. Experimental results illustrated that the HMCM improved performance of context-dependent fusion. These results suggest that spatial dependencies are an important source of contextual information for landmine detection that warrants further investigation. © 2010 IEEE.

Full Text

Duke Authors

Cited Authors

  • Ratto, C; Torrione, P; Morton, K; Collins, L

Published Date

  • December 1, 2010

Published In

  • International Geoscience and Remote Sensing Symposium (Igarss)

Start / End Page

  • 4192 - 4195

International Standard Book Number 13 (ISBN-13)

  • 9781424495665

Digital Object Identifier (DOI)

  • 10.1109/IGARSS.2010.5652297

Citation Source

  • Scopus