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    Smoothing/filtering LiDAR digital surface models. Experiments with loess regression and discrete wavelets


    Tate, Nicholas J. and Brunsdon, Chris and Charlton, Martin and Fotheringham, Stewart and Jarvis, Claire H (2005) Smoothing/filtering LiDAR digital surface models. Experiments with loess regression and discrete wavelets. Journal of Geographical Systems, 7 (3-4). pp. 273-290. ISSN 1435-5930

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    Abstract

    This paper reports on the smoothing/filtering analysis of a digital surface model (DSM) derived from LiDAR altimetry for part of the River Coquet, Northumberland, UK using loess regression and the 2D discrete wavelet transform (DWT) implemented in the S-PLUS and R statistical packages. The chosen method of analysis employs a simple method to gen- erate noise’ which is then added to a smooth sample of LiDAR data; loess regression and wavelet methods are then used to smooth/filter this data and compare with the original smooth’ sample in terms of RMSE. Various combinations of functions and parameters were chosen for both methods. Although wavelet analysis was effective in filtering the noise from the data, loess regression employing a quadratic parametric function produced the lowest RMSE and was the most effective.

    Item Type: Article
    Keywords: Digital surface model; LiDAR data;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 5901
    Identification Number: https://doi.org/10.1007/s10109-005-0007-4
    Depositing User: Martin Charlton
    Date Deposited: 23 Feb 2015 16:13
    Journal or Publication Title: Journal of Geographical Systems
    Publisher: Springer Verlag
    Refereed: Yes
    URI:

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