Dutta, R, Cohn, AG and Muggleton, JM (2013) 3D mapping of buried underworld infrastructure using dynamic Bayesian network based multi-sensory image data fusion. Journal of Applied Geophysics, 92. 8 - 19. ISSN 0926-9851
Abstract
The successful operation of buried infrastructure within urban environments is fundamental to the conservation of modern living standards. In this paper a novel multi-sensor image fusion framework has been proposed and investigated using dynamic Bayesian network for automatic detection of buried underworld infrastructure. Experimental multi-sensors images were acquired for a known buried plastic water pipe using Vibro-acoustic sensor based location methods and Ground Penetrating Radar imaging system. Computationally intelligent conventional image processing techniques were used to process three types of sensory images. Independently extracted depth and location information from different images regarding the target pipe were fused together using dynamic Bayesian network to predict the maximum probable location and depth of the pipe. The outcome from this study was very encouraging as it was able to detect the target pipe with high accuracy compared with the currently existing pipe survey map. The approach was also applied successfully to produce a best probable 3D buried asset map.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Dynamic Bayesian network; ground excitation; ground penetrating radar; image data fusion; pipe excitation; vibro-acoustic |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Mar 2015 15:36 |
Last Modified: | 03 Nov 2016 11:18 |
Published Version: | http://dx.doi.org/10.1016/j.jappgeo.2013.02.005 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jappgeo.2013.02.005 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83883 |