scholarly journals AUTOMATED UAV LIDAR STRIP ALIGNMENT IN FORESTED AREAS USING DENSITY-BASED CANOPY CLUSTERING

2020 ◽  
Vol V-1-2020 ◽  
pp. 81-88
Author(s):  
R. Fekry ◽  
W. Yao ◽  
L. Cao
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Research Work ◽  
Similarity Score ◽  
Scanning System ◽  
Matching Problem ◽  
Individual Tree ◽  
Tree Crowns ◽  
Key Points ◽  
Forested Areas

Abstract. Recently, LiDAR point cloud data acquired by Unmanned Aerial Vehicles (UAVs) are used in many scientific disciplines and like the former photogrammetric techniques these data are usually collected in overlapping strips. Generation of comprehensive models of the scanned areas requires these strips to be aligned together which is a challenging process due to the multi sensor scanning system including the scanning sensor, the GNSS receiver and the IMU sensor. The main errors result from the inaccurate GNSS locations and flight path shifts as well as failure of the GNSS signals in complex urban or forest environments. For that reasons, the development of an automatic feature-dependant method in urban areas or individual tree-based in forest areas where there are no distinct features for strip adjustment in these environments become a must. This research work focuses on automated co-registration/alignment multiple point cloud strips of forested areas acquired from UAV LiDAR (or referred to as ULS) lack of artificial ground control. The main limitations of ULS data of forests are the relatively low sampling density of near ground areas and stem nullity due to the top-view scanning mode of ULS. To obviate this, this work explicates the tree crowns shape to identify the key points required for co-registration by applying a density based clustering algorithm (DBSCAN) to the tree crowns and models resulting clusters with Gaussian mixture models by learning the best parameters using maximum likelihood estimation to define the key points. A feature vector is assigned to each point by quantifying its angular and linear relationship with respect to the local system origin. Next, the similarity score matrix is computed by a fixed geometric relationship between the distance and angle similarity. Then, the maximum weight matching problem is solved for the similarity score to gain point-to-point correspondence. Finally, the optimal 2D rigid transformation parameters (one rotation and two translations without scale factor ) are obtained using permutations to try out for all possible paired combinations and count the number of inlier points satisfying a tolerance of planimetric deviation after alignment within a user defined threshold. The results of two test forest plots with different tree species and ULS point densities show a mean planimetric enhancement from 1.79 m to 0.22 ± 0.13 m for plot one and from 2.33 ± 0.53 m to 0.61 ± 0.21 m.

scholarly journals Extracting Crown Morphology with a Low-Cost Mobile Lidar Scanning System in the Natural Environment

2021 ◽  
Vol 50 (11) ◽  
pp. 3193-3204
Author(s):  
Kai Wang ◽  
Jun Zhou ◽  
Wenhai Zhang ◽  
Baohua Zhang
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Low Cost ◽  
Correlation Coefficients ◽  
Scanning System ◽  
Morphological Parameters ◽  
Tight Coupling ◽  
Crown Height ◽  
Crown Diameter ◽  
Crown Volume

To meet the demand for intelligent measurements of canopy morphological parameters, a mobile LiDAR scanning system with LiDAR and IMU as the main sensors was constructed. The system uses a LiDAR-IMU tight coupling odometry method to reconstruct a point cloud map of the area surveyed. After using the RANSAC algorithm to remove the map ground, the European clustering algorithm is used for point cloud segmentation. Finally, morphological parameters of the canopy, such as crown height, crown diameter, and crown volume, are extracted using statistical and voxel methods. To verify the algorithm, a total of 43 trees in multiple plots of the campus were tested and compared. The algorithm defined in this study was evaluated with manual measurements as reference, and the morphological parameters of the canopy obtained using the LOAM and LeGO-LOAM algorithms as the basic framework were compared. Experiments show that this method can be used to easily obtain the crown height, crown diameter, and crown volume of the area; the correlation coefficients of these parameters were 0.91, 0.87, and 0.83, respectively. Compared with the LOAM and LeGO-LOAM methods, they were increased by 0.004, 0.12, and 0.13 and 0.07, 0.15, and 0.04, respectively. The test results for this new system are positive and meet the requirements of horticulture and orchard measurements, indicating that it will have significant value as an application.

scholarly journals ANALYSIS OF THE POSSIBILITIES OF USING LOW-COST SCANNING SYSTEM IN 3D MODELING

2016 ◽  
Vol XLI-B3 ◽  
pp. 261-267
Author(s):  
M. Kedzierski ◽  
D. Wierzbickia ◽  
A. Fryskowska ◽  
B. Chlebowska
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Laser Scanning ◽  
Low Cost ◽  
Comparative Method ◽  
Research Work ◽  
Laser Scanner ◽  
Method Comparison ◽  
Point Clouds ◽  
Scanning System

The laser scanning technique is still a very popular and fast growing method of obtaining information on modeling 3D objects. The use of low-cost miniature scanners creates new opportunities for small objects of 3D modeling based on point clouds acquired from the scan. The same, the development of accuracy and methods of automatic processing of this data type is noticeable. The article presents methods of collecting raw datasets in the form of a point-cloud using a low-cost ground-based laser scanner FabScan. As part of the research work 3D scanner from an open source FabLab project was constructed. In addition, the results for the analysis of the geometry of the point clouds obtained by using a low-cost laser scanner were presented. Also, some analysis of collecting data of different structures (made of various materials such as: glass, wood, paper, gum, plastic, plaster, ceramics, stoneware clay etc. and of different shapes: oval and similar to oval and prism shaped) have been done. The article presents two methods used for analysis: the first one - visual (general comparison between the 3D model and the real object) and the second one - comparative method (comparison between measurements on models and scanned objects using the mean error of a single sample of observations). The analysis showed, that the low-budget ground-based laser scanner FabScan has difficulties with collecting data of non-oval objects. Items built of glass painted black also caused problems for the scanner. In addition, the more details scanned object contains, the lower the accuracy of the collected point-cloud is. Nevertheless, the accuracy of collected data (using oval-straight shaped objects) is satisfactory. The accuracy, in this case, fluctuates between ± 0,4 mm and ± 1,0 mm whereas when using more detailed objects or a rectangular shaped prism the accuracy is much more lower, between 2,9 mm and ± 9,0 mm. Finally, the publication presents the possibility (for the future expansion of research) of modernization FabScan by the implementation of a larger amount of camera-laser units. This will enable spots the registration , that are less visible.

scholarly journals Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

2021 ◽  
Vol 13 (1) ◽  
pp. 705-716
Author(s):  
Qiuji Chen ◽  
Xin Wang ◽  
Mengru Hang ◽  
Jiye Li
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Tree Height ◽  
Mining Area ◽  
Research Area ◽  
Airborne Lidar ◽  
Shaanxi Province ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data

Abstract The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

scholarly journals ANALYSIS OF THE POSSIBILITIES OF USING LOW-COST SCANNING SYSTEM IN 3D MODELING

2016 ◽  
Vol XLI-B3 ◽  
pp. 261-267
Author(s):  
M. Kedzierski ◽  
D. Wierzbickia ◽  
A. Fryskowska ◽  
B. Chlebowska
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Laser Scanning ◽  
Low Cost ◽  
Comparative Method ◽  
Research Work ◽  
Laser Scanner ◽  
Method Comparison ◽  
Point Clouds ◽  
Scanning System

The laser scanning technique is still a very popular and fast growing method of obtaining information on modeling 3D objects. The use of low-cost miniature scanners creates new opportunities for small objects of 3D modeling based on point clouds acquired from the scan. The same, the development of accuracy and methods of automatic processing of this data type is noticeable. The article presents methods of collecting raw datasets in the form of a point-cloud using a low-cost ground-based laser scanner FabScan. As part of the research work 3D scanner from an open source FabLab project was constructed. In addition, the results for the analysis of the geometry of the point clouds obtained by using a low-cost laser scanner were presented. Also, some analysis of collecting data of different structures (made of various materials such as: glass, wood, paper, gum, plastic, plaster, ceramics, stoneware clay etc. and of different shapes: oval and similar to oval and prism shaped) have been done. The article presents two methods used for analysis: the first one - visual (general comparison between the 3D model and the real object) and the second one - comparative method (comparison between measurements on models and scanned objects using the mean error of a single sample of observations). The analysis showed, that the low-budget ground-based laser scanner FabScan has difficulties with collecting data of non-oval objects. Items built of glass painted black also caused problems for the scanner. In addition, the more details scanned object contains, the lower the accuracy of the collected point-cloud is. Nevertheless, the accuracy of collected data (using oval-straight shaped objects) is satisfactory. The accuracy, in this case, fluctuates between ± 0,4 mm and ± 1,0 mm whereas when using more detailed objects or a rectangular shaped prism the accuracy is much more lower, between 2,9 mm and ± 9,0 mm. Finally, the publication presents the possibility (for the future expansion of research) of modernization FabScan by the implementation of a larger amount of camera-laser units. This will enable spots the registration , that are less visible.

scholarly journals Leaf-Off and Leaf-On UAV LiDAR Surveys for Single-Tree Inventory in Forest Plantations

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 115
Author(s):  
Yi-Chun Lin ◽  
Jidong Liu ◽  
Songlin Fei ◽  
Ayman Habib
Keyword(s):  
Remote Sensing ◽  
Point Cloud ◽  
Clustering Algorithm ◽  
Point Clouds ◽  
Quality Data ◽  
Individual Tree ◽  
Single Tree ◽  
Density Based Clustering ◽  
Tree Inventory

LiDAR technology has been proven to be an effective remote sensing technique for forest inventory and management. Among existing remote sensing platforms, unmanned aerial vehicles (UAV) are rapidly gaining popularity for their capability to provide high-resolution and accurate point clouds. However, the ability of a UAV LiDAR survey to map under canopy features is determined by the degree of penetration, which in turn depends on the percentage of canopy cover. In this study, a custom-built UAV-based mobile mapping system is used for simultaneously collecting LiDAR and imagery data under different leaf cover scenarios in a forest plantation. Bare earth point cloud, digital terrain model (DTM), normalized height point cloud, and quantitative measures for single-tree inventory are derived from UAV LiDAR data. The impact of different leaf cover scenarios (leaf-off, partial leaf cover, and full leaf cover) on the quality of the products from UAV surveys is investigated. Moreover, a bottom-up individual tree localization and segmentation approach based on 2D peak detection and Voronoi diagram is proposed and compared against an existing density-based clustering algorithm. Experimental results show that point clouds from different leaf cover scenarios are in good agreement within a 1-to-10 cm range. Despite the point density of bare earth point cloud under leaf-on conditions being substantially lower than that under leaf-off conditions, the terrain models derived from the three scenarios are comparable. Once the quality of the DTMs is verified, normalized height point clouds that characterize the vertical forest structure can be generated by removing the terrain effect. Individual tree detection with an overall accuracy of 0.98 and 0.88 is achieved under leaf-off and partial leaf cover conditions, respectively. Both the proposed tree localization approach and the density-based clustering algorithm cannot detect tree trunks under full leaf cover conditions. Overall, the proposed approach outperforms the existing clustering algorithm owing to its low false positive rate, especially under leaf-on conditions. These findings suggest that the high-quality data from UAV LiDAR can effectively map the terrain and derive forest structural measures for single-tree inventories even under a partial leaf cover scenario.

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

Research on Signal Timing Method of Intersection Based on RFID

2011 ◽  
Vol 84-85 ◽  
pp. 410-414
Author(s):  
Wei He ◽  
Jie Xiong
Keyword(s):  
Comparative Analysis ◽  
Radio Frequency Identification ◽  
Main Parameter ◽  
Control Method ◽  
Research Work ◽  
Signal Timing ◽  
Key Points ◽  
Frequency Identification ◽  
New Research ◽  
Radio Frequency Identification Devices

Based on the RFID (Radio Frequency Identification Devices), this paper presents new research work of signal timing intersection in the aspects of the key points of design, the main parameter and timing methods. Finally, the intersection of signal timing of fuzzy control method based on RFID is proposed through comparative analysis of the intersection of several traditional signal timing methods.

Simplification algorithm of denture point cloud based on feature preserving

2021 ◽  
pp. 1-14
Author(s):  
Shigang Wang ◽  
Shuai Peng ◽  
Jiawen He
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Characteristic Point ◽  
Cloud Model ◽  
Average Distance ◽  
Normal Vector ◽  
Sampling Point ◽  
Characteristic Region ◽  
K Value ◽  
Feature Preserving

Due to the point cloud of oral scan denture has a large amount of data and redundant points. A point cloud simplification algorithm based on feature preserving is proposed to solve the problem that the feature preserving is incomplete when processing point cloud data and cavities occur in relatively flat regions. Firstly, the algorithm uses kd-tree to construct the point cloud spatial topological to search the k-Neighborhood of the sampling point. On the basis of that to calculate the curvature of each point, the angle between the normal vector, the distance from the point to the neighborhood centroid, as well as the standard deviation and the average distance from the point to the neighborhood on this basis, therefore, the detailed features of point cloud can be extracted by multi-feature extraction and threshold determination. For the non-characteristic region, the non-characteristic point cloud is spatially divided through Octree to obtain the K-value of K-means clustering algorithm and the initial clustering center point. The simplified results of non-characteristic regions are obtained after further subdivision. Finally, the extracted detail features and the reduced result of non-featured region will be merged to obtain the final simplification result. The experimental results show that the algorithm can retain the characteristic information of point cloud model better, and effectively avoid the phenomenon of holes in the simplification process. The simplified results have better smoothness, simplicity and precision, and are of high practical value.

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