Evaluation Of Roughness Parameters Research Articles

SEMI-AUTOMATED TREE SPECIES CLASSIFICATION BASED ON ROUGHNESS PARAMETERS USING AIRBORNE LIDAR DATA

Automated tree species classification using high density airborne LiDAR data supports precise forest inventory. This work shows a method based on evaluating roughness descriptors from aerial LiDAR data to automatically classify tree species. The proposed method includes treetops detection, neighbouring distance analysis for selecting the interest points, 3D fit surface creation, evaluation of roughness parameters, and K-means clustering. Among the evaluated roughness parameters, Skewness (Rsk) and Kurtosis (Rku) show robust classification. A synthetic point cloud was generated to test the methodology in a mixed forest formed by three tree species, Pinus sp., Quercus sp., and Eucalyptus sp. The Overall Accuracy (OA) of the classification method was 80 % for Quercus sp., 100 % for Pinus sp. and 80.6 % for Eucalyptus sp. In addition, the methodology was tested in three study areas and the results demonstrate that roughness parameters can be used to individual tree species classification in a mixed temperate forest with an OA of 82% in study area 1, 93 % in study area 2 and 92 % in study area 3. Keywords: aerial LiDAR, point cloud processing, tree species classification, spatial analysis, roughness parameters

Running-in and micropitting behaviour of steel surfaces under mixed lubrication conditions

The paper investigates the running-in of hardened steel surfaces under mixed lubrication conditions. Pairs of surfaces of both equal and differing hardness were loaded together under rolling/sliding conditions in a twin-disk rig, and the evolution of surface topography was investigated using in-situ profilometry. Evaluation of roughness parameters, height distributions and profile relocation showed that the running-in of these surfaces is a rapid process where the most prominent asperity tips undergo plastic deformation during the initial loading cycles. Finally, the pair of equal hardness disks, following further running in a separate series of experiments, was found to suffer from micro-pitting. This micropitting predominantly occurred along the tips of prominent asperities, and the potential link between running-in and surface failure is discussed.

Automatic classification of urban pavements using mobile LiDAR data and roughness descriptors

Maintenance of urban road pavements is shown as critical in city management. Mobile Laser Scanning is well-established for achieving geo-reference point clouds and imagery depicting the reality in an accurate and productive manner and intense efforts have been made to implement automatic methods for extracting geometric and semantic useful information. This work shows a method based on evaluating roughness descriptors from mobile LiDAR data to automatically segment and classifies asphalt and stone pavements. The method includes segmentation of 1m slices from the road, extraction of profiles, evaluation of roughness parameters, and K-means clustering. Among the evaluated roughness descriptors, only arithmetic average of absolute values (between 2.5·10−3m to 4·10−3m for stone and 10−3 to 2·10−3m for asphalt) and root mean squared (between 3·10−3m to 5·10−3m for stone and 10−3m to 2·10−3m for asphalt) show robust classification. The methodology is tested in three real case studies.

Episcopic coaxial illumination instrument for the simultaneous recording of the speckle signature in the energy spectrum and in the image of scattering reflective surfaces

The inspection of optically rough surfaces searching for defects or other macroscopic features, or with the aim of characterizing roughness, are tasks well suited to optical techniques. We present the concept, the architecture, the mathematical modeling, the calibration, and some results of a device with episcopic coaxial illumination, specifically developed for surface inspections, which simultaneously renders both a coherent image and the energy spectrum of the complex reflection coefficient of a portion of the surface, precisely delimited by the illuminating laser spot. This concept is based on the well-known, single-lens, coherent image processing setup with beamsplitters added to insert the illuminating beam and to allow simultaneous access to the Fourier transform plane and to the image plane. Information about the resolved macroscopic features and the nonresolved surface microstructure (through the corresponding speckle signature) is obtained in both planes which enables different surface analysis strategies. The speckle sizes in the spectrum, and in the image, can be controlled by selecting the size of the illuminated area of the object and the lens aperture, respectively. Some envisaged applications are the detection and characterization of defects or macroscopic structures in rough surfaces, identification of authentication marks, evaluation of roughness parameters, and of speckle statistics.

出水時複断面河道における横断方向水位変動特性の検討

Although water elevation is generally assumed to be uniform in the lateral direction, there has been little information on measured results for lateral variations of water elevation in rivers under floods. In the present study, we performed water elevation measurements at several cross-sections of the Edogawa River. Results showed that the differences of water elevation between the main channel and the floodplain that occurred during flooding were mainly due to a small mound that was formed between the main channel and the floodplain that prevented any water exchange between the two. The influence of lateral variations of water elevation on the evaluation of roughness parameters and river discharge was examined.