Dou, Q, Wei, L, Magee, D orcid.org/0000-0003-2170-3103 et al. (1 more author) (2017) Real-Time Hyperbola Recognition and Fitting in GPR Data. IEEE Transactions on Geoscience and Remote Sensing, 55 (1). pp. 51-62. ISSN 0196-2892
Abstract
The problem of automatically recognising and fitting hyperbolae from Ground Penetrating Radar (GPR) images is addressed, and a novel technique computationally suitable for real time on-site application is
proposed. After pre-processing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently,
a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm and a hyperbola is fitted to each such signature with an orthogonal distance hyperbola fitting algorithm. The
novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An
orthogonal distance hyperbola fitting algorithm for ‘south-opening’
hyperbolae is introduced in this work, which is more robust and accurate than algebraic hyperbola fitting algorithms. The proposed method can successfully recognise and fit hyperbolic signatures with intersections
with others, hyperbolic signatures with distortions and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulae to compute an initial ‘south-opening’ hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting
hyperbolae to hyperbolic signatures are very important features, they can be used to estimate the location, size of the related target objects, and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both
synthetic and real GPR data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Keywords: | Buried asset detection, column-connection clus- tering (C3) algorithm, ground-penetrating radar (GPR), hyper- bola recognition, machine learning, orthogonal-distance fitting. |
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) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/K021699/1 EPSRC (Engineering and Physical Sciences Research Council) EP/F06585X/1 EU - European Union 280712 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Jul 2016 10:58 |
Last Modified: | 20 Jun 2021 08:37 |
Published Version: | https://dx.doi.org/10.1109/TGRS.2016.2592679 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TGRS.2016.2592679 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:102528 |