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

Nicholas J. Tate, Chris Brunsdon, Martin Charlton, A. Stewart Fotheringham, Claire H. Jarvis

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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 generate 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.

Original languageEnglish (US)
Pages (from-to)273-290
Number of pages18
JournalJournal of Geographical Systems
Issue number3-4
StatePublished - Dec 2005
Externally publishedYes


  • Digital surface model
  • LiDAR data

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes


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