Influence of range measurement noise on roughness characterization of rock surfaces using terrestrial laser scanning

Khoshelham, Kourosh; Altundag, Dogan; Ngan-Tillard, D.J.M.; Menenti, Massimo

doi:10.1016/j.ijrmms.2011.09.007

Cited by 53 publications

(41 citation statements)

References 26 publications

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“…Applications on 3-D point cloud treatment dating back to the last decade will soon be integrated into SfM photogrammetry post-processing; examples include geomorphological investigations in high-mountain areas (Milan et al, 2007), geological mapping (Buckley et al, 2008;Franceschi et al, 2009), soil erosion studies , investigation of fluvial systems Cavalli et al, 2008;, and mass wasting phenomena (Lim et al, 2005;Oppikofer et al, 2009;Abellán et al, 2010). Some other data treatment techniques that have been developed during the last decade and that will be adapted and enriched by the growing SfM photogrammetry community include automatic lithological segmentation according to the intensity signature (Humair et al, 2015), integration of ground-based lidar with thermal/hyperspectral imaging for lithological discrimination (Kääb, 2008;Hartzell et al, 2014), extraction of the structural settings on a given outcrop (Jaboyedoff et al, 2007;Sturzenegger and Stead, 2009;Gigli and Casagli, 2011;Riquelme et al, 2014) and the automatic extraction of geological patterns such as surface roughness (Poropat, 2009) and discontinuity spacing/persistence/waviness (Fekete et al, 2010;Khoshelham et al, 2011;Pollyea and Fairley, 2011). Concerning 4-D data treatment for investigating changes on natural slope, some lessons learned may be adapted from the two-and threedimensional tracking of mass movements (Teza et al, 2007;Monserrat and Crosetto, 2008), investigation of progressive failures (Royan et al, 2015;Kromer et al, 2015), and from 376 A.…”

Section: Cross-disciplinaritymentioning

confidence: 99%

Image-based surface reconstruction in geomorphometry – merits, limits and developments

et al. 2016

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Abstract. Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost.The increasing computing capabilities during the last decade, together with the development of highperformance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice.This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.

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Section: Cross-disciplinaritymentioning

confidence: 99%

Image-based surface reconstruction in geomorphometry – merits, limits and developments

et al. 2016

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“…The number of publications has exponentially grown in the last years and has been able to successfully extract the orientation of discontinuities (Slob et al (2005); Olariu et al (2008); Sturzenegger and Stead (2009b); Sturzenegger et al (2011);Jaboyedoff et al (2007); García-Sellés et al (2011); Khoshelham et al (2011); Gigli and Casagli (2011); Lato and Vöge (2012); Riquelme et al (2014)). Once discontinu-…”

Section: Introductionmentioning

confidence: 99%

Discontinuity spacing analysis in rock masses using 3D point clouds

Riquelme

Abellán²,

Tomás

2015

Engineering Geology

127

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The complete characterization of rock masses implies the acquisition of information of both, the materials which compose the rock mass and the discontinuities which divide the outcrop. Recent advances in the use of remote sensing techniques -such as Light Detection and Ranging (LiDAR)-allow the accurate and dense acquisition of 3D information that can be used for the characterization of discontinuities. This work presents a novel methodology which allows the calculation of the normal spacing of persistent and non-persistent discontinuity sets using 3D point cloud datasets considering the three dimensional relationships between clusters.This approach requires that the 3D dataset has been previously classified. This implies that discontinuity sets are previously extracted, every single point is labelled with its corresponding discontinuity set and every exposed planar surface is analytically calculated. Then, for each discontinuity set the method calculates the normal spacing between an exposed plane and its nearest one considering 3D space relationship. This link between planes is obtained calculating for every point its nearest point member of the same discontinuity set, which provides its nearest plane. This allows to calculate the normal spacing for every plane. Finally, the normal spacing is calculated as the mean value of all the normal spacings for each discontinuity set. The methodology is validated through three cases of study using synthetic data and 3D laser scanning datasets. The first case illustrates the fundamentals and the performance of the proposed methodology. The second and the third cases of study correspond to two rock slopes for which datasets were acquired using a 3D laser scanner. The second case study has shown that results obtained from the traditional and the proposed approach are reasonably similar.Nevertheless, a discrepancy between both approaches has been found when the exposed planes members of a discontinuity set were hard to identify and when the planes pairing was difficult to establish during the fieldwork campaign. The third case study also has evidenced that when the number of identified exposed planes is high, the calculated normal spacing using the proposed approach is minor than those using the traditional approach.

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“…As proved in some recent studies (Bitenc et al, 2015a;Khoshelham et al, 2011;Smigiel et al, 2013Smigiel et al, , 2011Smigiel et al, , 2008 range error can be successfully reduced by image denoising methods. The TLS denoised surfaces show details, which may otherwise be lost in noise.…”

Section: Introductionmentioning

confidence: 80%

Evaluation of Wavelet and Non-Local Mean Denoising of Terrestrial Laser Scanning Data for Small-Scale Joint Roughness Estimation

Bitenc

Kieffer

Khoshelham

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Terrestrial Laser Scanning (TLS) is a well-known remote sensing tool that enables precise 3D acquisition of surface morphology from distances of a few meters to a few kilometres. The morphological representations obtained are important in engineering geology and rock mechanics, where surface morphology details are of particular interest in rock stability problems and engineering construction. The actual size of the discernible surface detail depends on the instrument range error (noise effect) and effective data resolution (smoothing effect). Range error can be (partly) removed by applying a denoising method. Based on the positive results from previous studies, two denoising methods, namely 2D wavelet transform (WT) and non-local mean (NLM), are tested here, with the goal of obtaining roughness estimations that are suitable in the context of rock engineering practice. Both methods are applied in two variants: conventional Discrete WT (DWT) and Stationary WT (SWT), classic NLM (NLM) and probabilistic NLM (PNLM). The noise effect and denoising performance are studied in relation to the TLS effective data resolution. Analyses are performed on the reference data acquired by a highly precise Advanced TOpometric Sensor (ATOS) on a 20x30 cm rock joint sample. Roughness ratio is computed by comparing the noisy and denoised surfaces to the original ATOS surface. The roughness ratio indicates the success of all denoising methods. Besides, it shows that SWT oversmoothes the surface and the performance of the DWT, NLM and PNLM vary with the noise level and data resolution. The noise effect becomes less prominent when data resolution decreases.

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Influence of range measurement noise on roughness characterization of rock surfaces using terrestrial laser scanning

Cited by 53 publications

References 26 publications

Image-based surface reconstruction in geomorphometry – merits, limits and developments

Image-based surface reconstruction in geomorphometry – merits, limits and developments

Discontinuity spacing analysis in rock masses using 3D point clouds

Evaluation of Wavelet and Non-Local Mean Denoising of Terrestrial Laser Scanning Data for Small-Scale Joint Roughness Estimation

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