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  • Digital terrain model height estimation using support vector machine regression

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    Ndehedehe221770-Published.pdf (411.5Kb)
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    Author(s)
    Okwuashi, Onuwa
    Ndehedehe, Christopher
    Griffith University Author(s)
    Ndehedehe, Christopher E.
    Year published
    2015
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    Abstract
    Digital terrain model interpolation is intrinsically a surface fitting problem, in which unknown heights H are estimated from known X-Y coordinates. Notable methods of digital terrain model interpolation include inverse distance to power, local polynomial, minimum curvature, modified Shepard's method, nearest neighbour and polynomial regression. We investigated the support vector machine regression (SVMR) as a new alternative method to these models. SVMR is a contemporary machine learning algorithm that has been applied to several real-world problems aside from digital terrain modelling. The SVMR results were compared with ...
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    Digital terrain model interpolation is intrinsically a surface fitting problem, in which unknown heights H are estimated from known X-Y coordinates. Notable methods of digital terrain model interpolation include inverse distance to power, local polynomial, minimum curvature, modified Shepard's method, nearest neighbour and polynomial regression. We investigated the support vector machine regression (SVMR) as a new alternative method to these models. SVMR is a contemporary machine learning algorithm that has been applied to several real-world problems aside from digital terrain modelling. The SVMR results were compared with those from notable parametric (the nearest neighbour) and non-parametric (the artificial neural network) techniques. Four categories of error analysis were used to assess the accuracy of the modelling: minimum error, maximum error, means error and standard error. The results indicate that SVMR furnished the lowest error, followed by the artificial neural network model. The SVMR also produced the smoothest surface followed by the artificial neural network model. The high accuracy furnished by SVMR in this experiment attests that SVMR is a promising model for digital terrain model interpolation.
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    Journal Title
    South African Journal of Science
    Volume
    111
    Issue
    9-10
    Publisher URI
    https://journals.co.za/doi/abs/10.17159/sajs.2015/20140153
    Copyright Statement
    © 2015. The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Science & Technology
    Multidisciplinary Sciences
    Science & Technology - Other Topics
    height estimation
    artificial neural network
    Publication URI
    http://hdl.handle.net/10072/401956
    Collection
    • Journal articles

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