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 Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

2019 ◽  
Vol 11 (11) ◽  
pp. 1263 ◽  
Author(s):  
Wen Xiao ◽  
Aleksandra Zaforemska ◽  
Magdalena Smigaj ◽  
Yunsheng Wang ◽  
Rachel Gaulton
Keyword(s):  
Mean Shift ◽  
Point Clouds ◽  
Forest Tree ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Tree ◽  
Mean Shift Algorithm ◽  
Detailed Assessment ◽  
The Mean ◽  
Airborne Lidar Data

Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements.

scholarly journals Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment

2020 ◽  
Vol 12 (6) ◽  
pp. 975 ◽  
Author(s):  
Jongho Park ◽  
Namhoon Cho
Keyword(s):  
Collision Avoidance ◽  
Collision Detection ◽  
Three Dimensional ◽  
Dynamic Environment ◽  
Point Clouds ◽  
Lidar Data ◽  
Lidar System ◽  
State Estimate ◽  
Aerial Vehicle ◽  
Moving Obstacle

A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

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 PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS

2016 ◽  
Vol XLI-B1 ◽  
pp. 245-251 ◽  
Author(s):  
C. Serifoglu ◽  
O. Gungor ◽  
V. Yilmaz
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Good Alternative ◽  
Lidar Data ◽  
Filtering Algorithms ◽  
Aerial Photos ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model ◽  
Uav Images

Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.

scholarly journals ACCURACY VALIDATION OF POINT CLOUDS OF UAV PHOTOGRAMMETRY AND ITS APPLICATION FOR RIVER MANAGEMENT

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 195-199 ◽  
Author(s):  
S. Kubota ◽  
Y. Kawai ◽  
R. Kadotani
Keyword(s):  
Human Resources ◽  
Three Dimensional ◽  
Heavy Rain ◽  
Point Clouds ◽  
River Management ◽  
Maintenance Management ◽  
Running Water ◽  
Operation And Maintenance ◽  
Aerial Vehicle ◽  
Accuracy Validation

River administration facilities such as levees and river walls play a major role in preventing flooding due to heavy rain. The forms of such facilities must be constantly monitored for alteration due to rain and running water, and limited human resources and budgets make it necessary to efficiently maintain river administration facilities. During maintenance, inspection results are commonly recorded on paper documents. Continuous inspection and repair using information systems are an on-going challenge. This study proposes a maintenance management system for river facilities that uses three-dimensional data to solve these problems and make operation and maintenance more efficient. The system uses three-dimensional data to visualize river facility deformation and its process, and it has functions that visualize information about river management at any point in the three-dimensional data. The three-dimensional data is generated by photogrammetry using a camera on an Unmanned Aerial Vehicle.

scholarly journals INDIVIDUAL TREE EXTRACTION FROM UAV LIDAR POINT CLOUDS BASED ON SELF-ADAPTIVE MEAN SHIFT SEGMENTATION

2021 ◽  
Vol V-1-2021 ◽  
pp. 25-30
Author(s):  
Z. Hui ◽  
N. Li ◽  
Y. Xia ◽  
P. Cheng ◽  
Y. He
Keyword(s):  
Mean Shift ◽  
Point Clouds ◽  
Extraction Methods ◽  
Parameter Setting ◽  
Segmentation Method ◽  
Individual Tree ◽  
Unman Aerial Vehicle ◽  
Aerial Vehicle ◽  
The Individual ◽  
Tree Extraction

Abstract. Unman aerial vehicle (UAV) LiDAR has been widely used in the field of forestry. Individual tree extraction is a key step for forest inventory. Although many individual tree extraction methods have been proposed, the individual tree extraction accuracy is still low due to the complex forest environments. Moreover, many parameters in these methods generally need to be set. Thus, the degree of automation of the methods is generally low. To solve these problems, this paper proposed an automatic mean shift segmentation method, in which the kernel bandwidths can be calculated self-adaptively. Meanwhile, a hierarchy mean shift segmentation technique was proposed to extract individual tree gradually. A plot-level UAV LiDAR tree dataset was adopted for testing the performance of the proposed method. Experimental results showed that the proposed method can achieve better individual tree extraction result without any parameter setting. Compared with the traditional mean shift segmentation method, both the completeness and mean accuracy of the proposed method are higher.

scholarly journals DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

2018 ◽  
Vol XLII-2 ◽  
pp. 407-410 ◽  
Author(s):  
E. Hadas ◽  
G. Jozkow ◽  
A. Walicka ◽  
A. Borkowski
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Apple Orchard ◽  
Lidar Data ◽  
Local Minima ◽  
Alpha Shape ◽  
Aerial Vehicle ◽  
Individual Trees

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200&amp;thinsp;points/m<sup>2</sup>) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92&amp;thinsp;% of trees in the test area. 6&amp;thinsp;% of trees in orchard were not separated from each other and 2&amp;thinsp;% were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09&amp;thinsp;m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

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