A hidden Markov context model for GPR-based landmine detection incorporating stick-breaking priors

Published

Conference Paper

In recent years, context-dependent algorithm fusion has been proposed for improving landmine detection with ground-penetrating radar (GPR) across changing environmental and operating conditions. While context-dependent fusion techniques generally assume independent observations, previous work showed that spatial information may be exploited by modeling context with a hidden Markov model (HMM). However, the degree of performance improvement was found to depend the number of states included in the HMM. In this work, stick-breaking priors were employed to automate learning of the number of HMM states, and therefore the number of contexts to consider. The improved spatially-dependent fusion technique was evaluated on GPR data collected over various targets at multiple test sites, and performance was compared to another context-dependent technique which assumed independent observations. Results illustrate the potential for nonparametric, spatially-dependent context modeling to exploit contextual information in sequentially-collected GPR data and improve overall classification performance. © 2011 IEEE.

Full Text

Duke Authors

Cited Authors

  • Ratto, CR; Morton, KD; Collins, LM; Torrione, PA

Published Date

  • November 16, 2011

Published In

  • International Geoscience and Remote Sensing Symposium (Igarss)

Start / End Page

  • 874 - 877

International Standard Book Number 13 (ISBN-13)

  • 9781457710056

Digital Object Identifier (DOI)

  • 10.1109/IGARSS.2011.6049270

Citation Source

  • Scopus