scholarly journals 3D Characterization of Sorghum Panicles Using a 3D Point Cloud Derived from UAV Imagery

2021 ◽  
Vol 13 (2) ◽  
pp. 282
Author(s):  
Anjin Chang ◽  
Jinha Jung ◽  
Junho Yeom ◽  
Juan Landivar
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Panicle Length ◽  
Color Ratio ◽  
High Resolution Imagery ◽  
Aerial Vehicle ◽  
Highly Correlated ◽  
Uav Images ◽  
Panicle Weight ◽  
Cylinder Fitting

Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed for assessing genetic diversity, variety selection, and yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed a detecting and characterizing method for individual sorghum panicles using a 3D point cloud derived from UAV images. The RGB color ratio was used to filter non-panicle points out and select potential panicle points. Individual sorghum panicles were detected using the concept of tree identification. Panicle length and width were determined from potential panicle points. We proposed cylinder fitting and disk stacking to estimate individual panicle volumes, which are directly related to yield. The results showed that the correlation coefficient of the average panicle length and width between the UAV-based and ground measurements were 0.61 and 0.83, respectively. The UAV-derived panicle length and diameter were more highly correlated with the panicle weight than ground measurements. The cylinder fitting and disk stacking yielded R2 values of 0.77 and 0.67 with the actual panicle weight, respectively. The experimental results showed that the 3D point cloud derived from UAV imagery can provide reliable and consistent individual sorghum panicle parameters, which were highly correlated with ground measurements of panicle weight.

scholarly journals Segmentation of UAV-based images incorporating 3D point cloud information

2015 ◽  
Vol XL-3/W2 ◽  
pp. 261-268 ◽  
Author(s):  
A. Vetrivel ◽  
M. Gerke ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Point Cloud ◽  
Region Growing ◽  
Image Space ◽  
Data Sets ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Aerial Vehicle ◽  
Uav Images ◽  
The Individual ◽  
3D Information

Numerous applications related to urban scene analysis demand automatic recognition of buildings and distinct sub-elements. For example, if LiDAR data is available, only 3D information could be leveraged for the segmentation. However, this poses several risks, for instance, the in-plane objects cannot be distinguished from their surroundings. On the other hand, if only image based segmentation is performed, the geometric features (e.g., normal orientation, planarity) are not readily available. This renders the task of detecting the distinct sub-elements of the building with similar radiometric characteristic infeasible. In this paper the individual sub-elements of buildings are recognized through sub-segmentation of the building using geometric and radiometric characteristics jointly. 3D points generated from Unmanned Aerial Vehicle (UAV) images are used for inferring the geometric characteristics of roofs and facades of the building. However, the image-based 3D points are noisy, error prone and often contain gaps. Hence the segmentation in 3D space is not appropriate. Therefore, we propose to perform segmentation in image space using geometric features from the 3D point cloud along with the radiometric features. The initial detection of buildings in 3D point cloud is followed by the segmentation in image space using the region growing approach by utilizing various radiometric and 3D point cloud features. The developed method was tested using two data sets obtained with UAV images with a ground resolution of around 1-2 cm. The developed method accurately segmented most of the building elements when compared to the plane-based segmentation using 3D point cloud alone.

scholarly journals UNMANNED AERIAL VEHICLE APPLICATIONS OF 3D MODELLING, VISUALIZATION AND PARAMETER CALCULATIONS

2018 ◽  
Vol XLII-5 ◽  
pp. 649-652 ◽  
Author(s):  
V. Lambey ◽  
A. D. Prasad
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
3D Modelling ◽  
Aerial Photogrammetry ◽  
Average Accuracy ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Institute Of Technology ◽  
Uav Images ◽  
Economical Alternative

<p><strong>Abstract.</strong> Photogrammetric surveying with the use of Unmanned Aerial Vehicles (UAV) have gained vast popularity in short span. UAV have the potential to provide imagery at an extraordinary spatial and temporal resolution when coupled with remote sensing. Currently, UAV platforms are fastest and easiest source of data for mapping and 3D modelling. It is to be considered as a low-cost substitute to the traditional airborne photogrammetry. In the present study, UAV applications are explored in terms of 3D modelling, visualization and parameter calculations. National Institute of Technology Raipur, Raipur is chosen as study area and high resolution images are acquired from the UAV with 85% overlap. 3D model is processed through the point cloud generated for the UAV images. The results are compared with traditional methods for validation. The average accuracy obtained for elevation points and area is 97.99% and 97.75%. The study proves that UAV based surveying is an economical alternative in terms of money, time and resources, when compared to the classical aerial photogrammetry methods.</p>

scholarly journals Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

2019 ◽  
Vol 11 (10) ◽  
pp. 1188
Author(s):  
Li Zheng ◽  
Yuhao Li ◽  
Meng Sun ◽  
Zheng Ji ◽  
Manzhu Yu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Necessary Condition ◽  
Control Points ◽  
The Road ◽  
Aerial Vehicle ◽  
Joint Mapping ◽  
Close Range ◽  
Uav Images

VLS (Vehicle-borne Laser Scanning) can easily scan the road surface in the close range with high density. UAV (Unmanned Aerial Vehicle) can capture a wider range of ground images. Due to the complementary features of platforms of VLS and UAV, combining the two methods becomes a more effective method of data acquisition. In this paper, a non-rigid method for the aerotriangulation of UAV images assisted by a vehicle-borne light detection and ranging (LiDAR) point cloud is proposed, which greatly reduces the number of control points and improves the automation. We convert the LiDAR point cloud-assisted aerotriangulation into a registration problem between two point clouds, which does not require complicated feature extraction and match between point cloud and images. Compared with the iterative closest point (ICP) algorithm, this method can address the non-rigid image distortion with a more rigorous adjustment model and a higher accuracy of aerotriangulation. The experimental results show that the constraint of the LiDAR point cloud ensures the high accuracy of the aerotriangulation, even in the absence of control points. The root-mean-square error (RMSE) of the checkpoints on the x, y, and z axes are 0.118 m, 0.163 m, and 0.084m, respectively, which verifies the reliability of the proposed method. As a necessary condition for joint mapping, the research based on VLS and UAV images in uncontrolled circumstances will greatly improve the efficiency of joint mapping and reduce its cost.

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 Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 95 ◽  
Author(s):  
Abdulla Al-Rawabdeh ◽  
Fangning He ◽  
Adel Moussa ◽  
Naser El-Sheimy ◽  
Ayman Habib
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Digital Imaging ◽  
Point Cloud ◽  
Imaging System ◽  
3D Point Cloud ◽  
Digital Imaging System ◽  
Aerial Vehicle

scholarly journals Drone-Based Participatory Mapping: Examining Local Agricultural Knowledge in the Galapagos

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 62
Author(s):  
Mia Colloredo-Mansfeld ◽  
Francisco J. Laso ◽  
Javier Arce-Nazario
Keyword(s):  
Management Strategies ◽  
Agricultural Practices ◽  
Invasive Species Management ◽  
Farming Practices ◽  
Spatial And Temporal Scales ◽  
Agricultural Knowledge ◽  
High Resolution Imagery ◽  
Aerial Vehicle ◽  
Uav Images ◽  
On Farm

Agriculture is cultural heritage, and studies of agricultural spaces and practices help this heritage to be valued and protected. In the Galapagos Islands, little focus has been placed on local agricultural practices and agroforestry, despite their increasing importance for food security and invasive species management. This article discusses the possibilities for unoccupied aerial vehicle (UAV) high-resolution imagery in examining agricultural and agroforestry spaces, techniques, and practices. It describes and assesses an UAV-assisted participatory methodology for on-farm qualitative research that aims to investigate the visible and invisible features of farming practices. An analysis of the types of responses elicited by different methods of interviews with Galapagos farmers demonstrates how incorporating UAV data affects what we took away from the interview, and how the perceived relationship between farmer and land is reflected. Specifically, we find that when interacting with orthomosaics created from UAV images of their farms, farmers’ responses reveal a greater focus on management strategies at larger spatial and temporal scales. UAV imagery thus supports studies of agricultural heritage not only by recording agricultural spaces but also by revealing agrarian knowledge and practices.

scholarly journals Accuracy Evaluation of 3D Geometry from Low-Attitude UAV collections A case at Zijin Mine

2014 ◽  
Vol XL-4 ◽  
pp. 297-300 ◽  
Author(s):  
Q. Wang ◽  
L. Wu ◽  
S. Chen ◽  
D. Shu ◽  
Z. Xu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Experimental Result ◽  
Surface Model ◽  
Accuracy Evaluation ◽  
3D Point Cloud ◽  
Ground Control Points ◽  
Aerial Vehicle ◽  
Modelling Techniques ◽  
Image Modelling

This study investigates the usability of low-attitude unmanned aerial vehicle (UAV) acquiring high resolution images for the geometry reconstruction of opencast mine. Image modelling techniques like Structure from Motion (SfM) and Patch-based Multiview Stereo (PMVS) algorithms are used to generate dense 3D point cloud from UAV collections. Then, precision of 3D point cloud will be first evaluated based on Real-time Kinematic (RTK) ground control points (GCPs) at point level. The experimental result shows that the mean square error of the UAV point cloud is 0.11 m. Digital surface model (DSM) of the study area is generated from UAV point cloud, and compared with that from the Terrestrial Laser Scanner (TLS) data for further comparison at the surface level. Discrepancy map of 3D distances based on DSMs shows that most deviation is less than ±0.4 m and the relative error of the volume is 1.55 %.

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 Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures

2020 ◽  
Vol 12 (18) ◽  
pp. 2873 ◽  
Author(s):  
Elisa Mariarosaria Farella ◽  
Alessandro Torresani ◽  
Fabio Remondino
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Typical Result ◽  
Dense Point ◽  
Quality Checks ◽  
Aerial Vehicle ◽  
3D Processing ◽  
Uav Images ◽  
Image Orientation ◽  
Dense 3D Reconstruction

The paper presents an efficient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D representation of large and complex scenarios. Nevertheless, their photogrammetric integration often raises several issues in the image orientation and dense 3D reconstruction processes. Noisy and erroneous 3D reconstructions are the typical result of inaccurate orientation results. In this work, we propose an automatic filtering procedure which works at the sparse point cloud level and takes advantage of photogrammetric quality features. The filtering step removes low-quality 3D tie points before refining the image orientation in a new adjustment and generating the final dense point cloud. Our method generalizes to many datasets, as it employs statistical analyses of quality feature distributions to identify suitable filtering thresholds. Reported results show the effectiveness and reliability of the method verified using both internal and external quality checks, as well as visual qualitative comparisons. We made the filtering tool publicly available on GitHub.

scholarly journals Evaluación de la estabilidad de taludes rocosos a partir de nubes de puntos 3D obtenidas con un vehículo aéreo no tripulado

2020 ◽  
pp. 1
Author(s):  
R. Tomás ◽  
A. Riquelme ◽  
M. Cano ◽  
J. L. Pastor ◽  
J. I. Pagán ◽  
...  
Keyword(s):  
Open Source Software ◽  
Structure From Motion ◽  
Point Cloud ◽  
Geometric Analysis ◽  
3D Point Cloud ◽  
Digital Technique ◽  
Stability Analyses ◽  
Railway Line ◽  
Discontinuity Sets ◽  
Aerial Vehicle

<p>In this work, a methodology proposed for the semiautomatic identification of discontinuities and the later kinematic and stability analyses is described through its application to a rocky railway line cutting. Image acquisition has been performed using a six-rotors unmanned aerial vehicle for their subsequent photogrammetric restitution by means of the digital technique Structure from Motion (SfM) by means of the software Agisoft Metashape that provides a 3D point cloud. From this 3D point cloud, four discontinuity sets (J<sub>1</sub>, J<sub>2</sub>, J<sub>3</sub> and J<sub>4</sub>) affecting the cutting have been identified using the open source software Discontinuity Set Extractor (DSE). Finally, kinematic and stability analyses of the potential block failures controlled by the discontinuities identified in the cutting. The results show three potential wedge and planar failures that have been qualitatively validated trough the geometric analysis of the 3D point cloud.<em></em></p>

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