scholarly journals Spherically Stratified Point Projection: Feature Image Generation for Object Classification Using 3D LiDAR Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7860
Author(s):  
Chulhee Bae ◽  
Yu-Cheol Lee ◽  
Wonpil Yu ◽  
Sejin Lee
Keyword(s):  
Three Dimensional ◽  
Object Classification ◽  
Point Clouds ◽  
Lidar Data ◽  
Image Generation ◽  
Channel Integration ◽  
Occupancy Probability ◽  
Evidence Collection ◽  
Data Points ◽  
Point Projection

Three-dimensional point clouds have been utilized and studied for the classification of objects at the environmental level. While most existing studies, such as those in the field of computer vision, have detected object type from the perspective of sensors, this study developed a specialized strategy for object classification using LiDAR data points on the surface of the object. We propose a method for generating a spherically stratified point projection (sP2) feature image that can be applied to existing image-classification networks by performing pointwise classification based on a 3D point cloud using only LiDAR sensors data. The sP2’s main engine performs image generation through spherical stratification, evidence collection, and channel integration. Spherical stratification categorizes neighboring points into three layers according to distance ranges. Evidence collection calculates the occupancy probability based on Bayes’ rule to project 3D points onto a two-dimensional surface corresponding to each stratified layer. Channel integration generates sP2 RGB images with three evidence values representing short, medium, and long distances. Finally, the sP2 images are used as a trainable source for classifying the points into predefined semantic labels. Experimental results indicated the effectiveness of the proposed sP2 in classifying feature images generated using the LeNet architecture.

scholarly journals ROAD SURFACE DETECTION FROM MOBILE LIDAR DATA

2018 ◽  
Vol IV-5 ◽  
pp. 95-101 ◽  
Author(s):  
M. Yadav ◽  
B. Lohani ◽  
A. K. Singh
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Test Site ◽  
Road Surface ◽  
Lidar Data ◽  
Rural Roads ◽  
The Road ◽  
Surface Detection ◽  
Rural Road ◽  
Data Points

<p><strong>Abstract.</strong> The accurate three-dimensional road surface information is highly useful for health assessment and maintenance of roads. It is basic information for further analysis in several applications including road surface settlement, pavement condition assessment and slope collapse. Mobile LiDAR system (MLS) is frequently used now a days to collect detail road surface and its surrounding information in terms three-dimensional (3D) point cloud. Extraction of road surface from volumetric point cloud data is still in infancy stage because of heavy data processing requirement and the complexity in the road environment. The extraction of roads especially rural road, where road-curb is not present is very tedious job especially in Indian roadway settings. Only a few studies are available, and none for Indian roads, in the literature for rural road detection. The limitations of existing studies are in terms of their lower accuracy, very slow speed of data processing and detection of other objects having similar characteristics as the road surface. A fast and accurate method is proposed for LiDAR data points of road surface detection, keeping in mind the essence of road surface extraction especially for Indian rural roads. The Mobile LiDAR data in <i>XYZI</i> format is used as input in the proposed method. First square gridding is performed and ground points are roughly extracted. Then planar surface detection using mathematical framework of principal component analysis (PCA) is performed and further road surface points are detected using similarity in intensity and height difference of road surface pointe in their neighbourhood.</p><p>A case study was performed on the MLS data points captured along wide-street (two-lane road without curb) of 156<span class="thinspace"></span>m length along rural roadway site in the outskirt of Bengaluru city (South-West of India). The proposed algorithm was implemented on the MLS data of test site and its performance was evaluated it terms of recall, precision and overall accuracy that were 95.27%, 98.85% and 94.23%, respectively. The algorithm was found computationally time efficient. A 7.6 million MLS data points of size 27.1<span class="thinspace"></span>MB from test site were processed in 24 minutes using the available computational resources. The proposed method is found to work even for worst case scenarios, i.e., complex road environments and rural roads, where road boundary is not clear and generally merged with road-side features.</p>

scholarly journals MULTI-SCALE BASED EXTRACION OF VEGETATION FROM TERRESTRIAL LiDAR DATA FOR ASSESSING LOCAL LANDSCAPE

2015 ◽  
Vol II-3/W4 ◽  
pp. 263-270 ◽  
Author(s):  
T. Wakita ◽  
J. Susaki
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Point Clouds ◽  
Lidar Data ◽  
Data Sets ◽  
Landscape Index ◽  
Terrestrial Lidar ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Set Of Points

In this study, we propose a method to accurately extract vegetation from terrestrial three-dimensional (3D) point clouds for estimating landscape index in urban areas. Extraction of vegetation in urban areas is challenging because the light returned by vegetation does not show as clear patterns as man-made objects and because urban areas may have various objects to discriminate vegetation from. The proposed method takes a multi-scale voxel approach to effectively extract different types of vegetation in complex urban areas. With two different voxel sizes, a process is repeated that calculates the eigenvalues of the planar surface using a set of points, classifies voxels using the approximate curvature of the voxel of interest derived from the eigenvalues, and examines the connectivity of the valid voxels. We applied the proposed method to two data sets measured in a residential area in Kyoto, Japan. The validation results were acceptable, with F-measures of approximately 95% and 92%. It was also demonstrated that several types of vegetation were successfully extracted by the proposed method whereas the occluded vegetation were omitted. We conclude that the proposed method is suitable for extracting vegetation in urban areas from terrestrial light detection and ranging (LiDAR) data. In future, the proposed method will be applied to mobile LiDAR data and the performance of the method against lower density of point clouds will be examined.

scholarly journals Automated Three-Dimensional Linear Elements Extraction from Mobile LiDAR Point Clouds in Railway Environments

2019 ◽  
Vol 4 (3) ◽  
pp. 46
Author(s):  
Luis Gézero ◽  
Carlos Antunes
Keyword(s):  
Three Dimensional ◽  
Simple Algorithm ◽  
Point Clouds ◽  
Lidar Data ◽  
Quality Information ◽  
Geometric Methods ◽  
Restricted Number ◽  
System Trajectory ◽  
Linear Elements ◽  
Cloud Points

The railway structures need constant monitoring and maintenance to ensure the train circulation safety. Quality information concerning the infrastructure geometry, namely the three-dimensional linear elements, are crucial for that processes. Along with this work, a method to automated extract three-dimensional linear elements from point clouds collected by terrestrial mobile LiDAR systems along railways is presented. The proposed method takes advantage of the stored cloud point’s attributes as an alternative to complex geometric methods applied over the point’s cloud coordinates. Based on the assumption that the linear elements to extract are roughly parallel to the rail tracks and therefore to the system trajectory, the stored scan angle value was used to restrict the number of cloud points that represents the linear elements. A simple algorithm is then applied to that restricted number of points to get the three-dimensional polylines geometry. The obtained values of completeness, correctness and quality, validate the use of the methodology for linear elements extraction from mobile LiDAR data gathered along railway environments.

scholarly journals Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair Descriptor

2018 ◽  
Vol 3 (2) ◽  
pp. 865-872 ◽  
Author(s):  
Dmytro Bobkov ◽  
Sili Chen ◽  
Ruiqing Jian ◽  
Muhammad Z. Iqbal ◽  
Eckehard Steinbach
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Object Classification ◽  
Point Clouds ◽  
Point Pair

scholarly journals Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 35 ◽  
Author(s):  
Jonathan P. Resop ◽  
Laura Lehmann ◽  
W. Cully Hession
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Lidar Data ◽  
Lower Altitude ◽  
Aerial Laser Scanning ◽  
Digital Terrain ◽  
Terrain Models ◽  
Data Points ◽  
Aerial Vehicle ◽  
Complete Survey

Lidar remote sensing has been used to survey stream channel and floodplain topography for decades. However, traditional platforms, such as aerial laser scanning (ALS) from an airplane, have limitations including flight altitude and scan angle that prevent the scanner from collecting a complete survey of the riverscape. Drone laser scanning (DLS) or unmanned aerial vehicle (UAV)-based lidar offer ways to scan riverscapes with many potential advantages over ALS. We compared point clouds and lidar data products generated with both DLS and ALS for a small gravel-bed stream, Stroubles Creek, located in Blacksburg, VA. Lidar data points were classified as ground and vegetation, and then rasterized to produce digital terrain models (DTMs) representing the topography and canopy height models (CHMs) representing the vegetation. The results highlighted that the lower-altitude, higher-resolution DLS data were more capable than ALS of providing details of the channel profile as well as detecting small vegetation on the floodplain. The greater detail gained with DLS will provide fluvial researchers with better estimates of the physical properties of riverscape topography and vegetation.

scholarly journals An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1999 ◽  
Author(s):  
Wanqian Yan ◽  
Haiyan Guan ◽  
Lin Cao ◽  
Yongtao Yu ◽  
Sha Gao ◽  
...  
Keyword(s):  
Mean Shift ◽  
Three Dimensional ◽  
Terrestrial Ecosystems ◽  
Point Clouds ◽  
Lidar Data ◽  
Mean Shift Algorithm ◽  
Robust Segmentation ◽  
Forest Ecosystem Services ◽  
Segmentation Approach ◽  
Aerial Vehicle

Forests play a key role in terrestrial ecosystems, and the variables extracted from single trees can be used in various fields and applications for evaluating forest production and assessing forest ecosystem services. In this study, we developed an automated hierarchical single-tree segmentation approach based on the high density three-dimensional (3D) Unmanned Aerial Vehicle (UAV) point clouds. First, this approach obtains normalized non-ground UAV points in data preprocessing; then, a voxel-based mean shift algorithm is used to roughly classify the non-ground UAV points into well-detected and under-segmentation clusters. Moreover, potential tree apices for each under-segmentation cluster are obtained with regard to profile shape curves and finally input to the normalized cut segmentation (NCut) algorithm to segment iteratively the under-segmentation cluster into single trees. We evaluated the proposed method using datasets acquired by a Velodyne 16E LiDAR system mounted on a multi-rotor UAV. The results showed that the proposed method achieves the average correctness, completeness, and overall accuracy of 0.90, 0.88, and 0.89, respectively, in delineating single trees. Comparative analysis demonstrated that our method provided a promising solution to reliable and robust segmentation of single trees from UAV LiDAR data with high point cloud density.

scholarly journals Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

2020 ◽  
Vol 12 (9) ◽  
pp. 1363 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Jingmin Tu ◽  
Xinyi Liu ◽  
Yinxuan Li ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Models ◽  
Novel Approach ◽  
Airborne Lidar Data ◽  
Seed Points

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

scholarly journals VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6735 ◽  
Author(s):  
Zishu Liu ◽  
Wei Song ◽  
Yifei Tian ◽  
Sumi Ji ◽  
Yunsick Sung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Object Classification ◽  
Point Clouds ◽  
Learning System ◽  
Learning Networks ◽  
3D Object ◽  
Feature Extractor ◽  
Irregular Point ◽  
3D Object Classification

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.

scholarly journals Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates

2020 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Luiz Felipe Ramalho de Oliveira ◽  
H. Andrew Lassiter ◽  
Ben Wilkinson ◽  
Travis Whitley ◽  
Peter Ifju ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Three Dimensional ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Tree ◽  
Tree Detection

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

scholarly journals A VOXEL-BASED FILTERING ALGORITHM FOR MOBILE LIDAR DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1433-1438 ◽  
Author(s):  
H. Qin ◽  
G. Guan ◽  
Y. Yu ◽  
L. Zhong
Keyword(s):  
Three Dimensional ◽  
Block Size ◽  
Point Clouds ◽  
Multiple Point ◽  
Lidar Data ◽  
Filtering Algorithm ◽  
Promising Solution ◽  
Overall Performance ◽  
Partition Structure ◽  
Global And Local

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

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