Volume II-5/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 31-36, 2013
https://doi.org/10.5194/isprsannals-II-5-W2-31-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 31-36, 2013
https://doi.org/10.5194/isprsannals-II-5-W2-31-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 Oct 2013

16 Oct 2013

A signal denoising method for full-waveform LiDAR data

M. Azadbakht2,1, C. S. Fraser2,1, C. Zhang3,1, and J. Leach2 M. Azadbakht et al.
  • 1Cooperative Research Centre for Spatial Information, VIC 3053, Australia
  • 2Department of Infrastructure Engineering, University of Melbourne, VIC 3010, Australia
  • 3School of Mathematical and Spatial Sciences, RMIT University, VIC 3000, Australia

Keywords: Savitzky-Golay method, signal distortion, retrieval, Full-waveform LiDAR, Active Remote Sensing

Abstract. The lack of noise reduction methods resistant to waveform distortion can hamper correct and accurate decomposition in the processing of full-waveform LiDAR data. This paper evaluates a time-domain method for smoothing and reducing the noise level in such data. The Savitzky-Golay (S-G) approach approximates and smooths data by taking advantage of fitting a polynomial of degree d, using local least-squares. As a consequence of the integration of this method with the Singular Value Decomposition (SVD) approach, and applying this filter on the singular vectors of the SVD, satisfactory denoising results can be obtained. The results of this SVD-based S-G approach have been evaluated using two different LiDAR datasets and also compared with those of other popular methods in terms of the degree of preservation of the moments of the signal and closeness to the noisy signal. The results indicate that the SVD-based S-G approach has superior performance in denoising full-waveform LiDAR data.