Range Versus Surface Denoising of Terrestrial Laser Scanning Data for Rock Discontinuity Roughness Estimation

Abstract

Surface roughness represents a major component of rock discontinuity shear strength. To achieve comprehensive, accurate, and efficient estimates of in situ discontinuity roughness, the traditional contact measuring methods are being replaced by advanced remote-sensing technologies. Terrestrial laser scanner (TLS) is well suited for measuring large inaccessible discontinuities; however, inherent TLS range noise strongly influences the surface details and roughness estimation. The aim of this research is to establish an optimal wavelet-denoising procedure for the TLS data acquired with different scanning configurations (range and incidence angle), and for rock discontinuities having different roughness characteristics and surface reflectivity. The conventional discrete wavelet transform and stationary wavelet transform in combination with four threshold selection methods are applied on TLS data in the direction of range measurements (range denoising) and in the direction perpendicular to the best-fit plane (surface denoising). The performance of the denoising procedures is assessed by comparing the range and surface-denoised TLS surfaces with reference surfaces acquired with the Advanced TOpometric Sensor. Comparative analyses of the roughness calculated according to the angular thresholding method (Grasselli, in Shear strength of rock joints based on quantified surface description, Ph.D. thesis. EPF Lausanne, Lausanne; Grasselli, Shear strength of rock joints based on quantified surface description, Ph.D. thesis, EPF Lausanne, Lausanne, 2001) indicate that all the denoising methods improve the roughness estimated from the TLS data appreciably; however, the level of improvement depends intrinsically on geometrical characteristics of the rock surface and scanning configuration. Range denoising has been found to provide more reliable noise estimations.

Potential and Limitations of Terrestrial Laser Scanning for Discontinuity Roughness Estimation

“…The experiment with simulated TLS data, when a random Gaussian noise has been added to the reference data, has shown that the equals the known noise level (Bitenc et al, 2015a). The investigation of dependency on the recorded intensity (Bitenc et al, 2019), has confirmed the one term power model as developed in (Wujanz et al, 2017);…”

“…For all samples, tends to increase with the R. Exception is the low for sample 1309 scanned at 60 m. Possible reason for this anomaly includes the low original point density of 2 mm compared to 1 mm range image pixel size. The is higher for darker rock surfaces of samples 0727 and 0934, because of lower backscattered intensity, than for the brighter samples 0936 and 1309 (Bitenc et al, 2019). Fig.…”

“…To improve surface roughness estimation, previous researchers investigated a transforming domain filtering method, namely the DWT (Bitenc et al, 2015a(Bitenc et al, , 2015b(Bitenc et al, , 2019Khoshelham et al, 2011), and a spatial (pixel) domain filtering method, namely the Non-Local Mean (NLM) (Bitenc et al, 2016;Smigiel et al, 2011). Prior research has focused on denoising in a direction perpendicular to the best fit plane.…”

“…Details on the wavelet denoising can be found in numerous literature, among others in (Donoho, 1995). Based on our previous research (Bitenc et al, 2019), the optimal choices for DWT method and threshold value are as follows.…”

“…Possible reasons for underestimated roughness of rougher samples 0936 and 1309 scanned from longer ranges are firstly, high thresholds remove also some surface details and secondly, the effective resolution, which decreases with the R, additionally smoothes surface details. High for sample 0936 scanned from 10 m could be a result of higher noise due to the strong reflection (Bitenc et al, 2019). and std(Δ , ) for denoised TLS surfaces acquired at scanning ranges R k and reference grid sizes λ j .…”