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

A signal denoising method for full-waveform LiDAR data