WAVELET DE-NOISING OF TERRESTRIAL LASER SCANNER DATA FOR THE CHARACTERIZATION OF ROCK SURFACE ROUGHNESS

摘要

The application of terrestrial laser scanning to the study of rock surface roughness faces a major challenge: the inherent range imprecision makes the extraction of roughness parameters difficult. In practice, when roughness is in millimeter scale it is often lost in the range measurement noise. The parameters extracted from the data, therefore, reflect noise rather than the actual roughness of the surface. In this paper we investigate the role of wavelet de-noising methods in the reliable characterization of roughness using laser range data. The application of several wavelet decomposition and thresholding methods are demonstrated, and the performances of these methods in estimating roughness parameters are compared. As the main measure of roughness fractal dimension is derived from 1D profiles in different directions using the roughness length method. It is shown that wavelet de-noising in general leads to an improved estimation of the fractal dimension for the roughness profiles. The choice of the decomposition method is shown to have a minor effect on the de-noising results; however, the application of hard or soft thresholding mode does have a considerable influence on the estimated roughness measures. The presented results suggest that hard thresholding yields more accurate de-noised profiles for which the estimated roughness measures are more reliable.