EMI-based classification of multiple closely spaced subsurface objects via independent component analysis

Published

Journal Article

Previous work in subsurface object discrimination using electromagnetic induction data has shown that discrimination algorithms based on statistical signal processing techniques are effective for classifying data from objects that occur in isolation. However, for multiple closely spaced subsurface objects, the raw (unprocessed) measurement is a mixture of the responses from several objects and as such cannot be used directly to determine the identity of each of the individual objects. Thus, we propose to separate individual signatures from the mixture by posing the problem as a blind source separation (BSS) problem and effecting signature separation using independent component analysis. We propose to apply BSS to separate the mixed signatures and then follow the separation process with a Bayesian classifier. This approach is evaluated using both simulated data and data from unexploded ordnance items. The results show that this approach can be used to effectively classify multiple closely spaced objects.

Full Text

Duke Authors

Cited Authors

  • Hu, W; Tantum, SL; Collins, LM

Published Date

  • November 1, 2004

Published In

Volume / Issue

  • 42 / 11

Start / End Page

  • 2544 - 2554

International Standard Serial Number (ISSN)

  • 0196-2892

Digital Object Identifier (DOI)

  • 10.1109/TGRS.2004.835223

Citation Source

  • Scopus