scholarly journals Assessment of DSM Based on Radiometric Transformation of UAV Data

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1649
Author(s):  
Muhammad Hamid Chaudhry ◽  
Anuar Ahmad ◽  
Qudsia Gulzar ◽  
Muhammad Shahid Farid ◽  
Himan Shahabi ◽  
...  
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Image Data ◽  
Flying Height ◽  
Surface Model ◽  
Lidar Data ◽  
Local Accuracy ◽  
Uav Images ◽  
Uav Image

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

scholarly journals Modelling 3D Topography by Comparing Airborne Lidar Data with Unmanned Aerial System (UAS) Photogrammetry Under Multiple Imaging Conditions

2020 ◽  
Vol 6 (2) ◽  
pp. 122-138
Author(s):  
Oluibukun Gbenga Ajayi ◽  
Mark Palmer
Keyword(s):  
Accuracy Assessment ◽  
Image Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multiple Imaging ◽  
Significant Difference ◽  
Lidar Dem ◽  
Vertical Accuracy ◽  
Uav Images ◽  
Airborne Lidar Data

This study presents the effect of image data sources on the topographic modelling of part of the National Trust site located at Weston-Super-Mare, Bristol, United Kingdom, covering an approximate area of 1.82 hectares. The accuracy of the DEM generated from 1m resolution and 2m resolution LiDAR data together with the accuracy of the DEM generated from the UAV images acquired at different altitudes are analysed using the 1 m LiDAR DEM as reference for the accuracy assessment. Using the NSSDA methodology, the horizontal and vertical accuracy of the DEMs generated from each of the four sources were computed while the paired sample t-test was conducted to ascertain the existence of statistically significant difference between the means of the X, Y, and Z coordinates of the check points. The result obtained shows that with a RMSE of -0.0101499 and horizontal accuracy of -0.175674686m, the planimetric coordinates extracted from 2 m LiDAR DEM were more accurate than the planimetric coordinates extracted from the UAV based DEMs while the UAV based DEMs proved to be more accurate than the 2m LiDAR DEM in terms of altimetric coordinates, though the DEM generated from UAV images acquired at 50 m altitude gave the most accurate result when compared with the vertical accuracy obtained from the DEM generated from UAV images acquired at 30 m and 70 m flight heights. These findings are also consistent with the result of the statistical analysis at 95% confidence interval.

scholarly journals Method of Defining the Parameters for UAV Point Cloud Classification Algorithm

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Bui NGOC QUY ◽  
Le DINH HIEN ◽  
Nguyen QUOC LONG ◽  
Tong SI SON ◽  
Duong ANH QUAN ◽  
...  
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
3D Models ◽  
Open Pit ◽  
Surface Model ◽  
Point Cloud Data ◽  
Secondary Products ◽  
Cloud Data ◽  
Uav Images ◽  
Point Cloud Classification

Image data from Drones/Unmanned Aerial Vehicles (UAVs) has been studied and used extensively for establishing maps. The process of UAV data provides three main products including (Digital Surface Model) DSM, Point cloud and Ortho-photos, in which point cloud is a valuable data source in building 3D models and topographic surfaces as well. However, processing point cloud separately to achieve secondary products has not been received much attention from researchers. This study determines parameters to develop a method for classifying point cloud data constructed from UAV images. Consequently, A 3D surface of the ground is built by applying a developed algorithm for the point cloud data for an open-pit mine. The temporal or non-ground objects such as trees, houses, vehicles are automatically subtracted from the point cloud by the algorithms.  According to this line, it is possible to calculate and analyze the amount of reserves, the exploited volume to evaluate the efficiency for each mine during operation with the support of UAV integrated camera.

Mapping with Small UAS: A Point Cloud Accuracy Assessment

2015 ◽  
Vol 9 (4) ◽  
Author(s):  
Charles Toth ◽  
Grzegorz Jozkow ◽  
Dorota Grejner-Brzezinska
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Image Sensor ◽  
Flying Height ◽  
Attractive Alternative ◽  
Surface Model ◽  
Topographic Mapping ◽  
Main Question ◽  
Essential Information

AbstractInterest in using inexpensive Unmanned Aerial System (UAS) technology for topographic mapping has recently significantly increased. Small UAS platforms equipped with consumer grade cameras can easily acquire high-resolution aerial imagery allowing for dense point cloud generation, followed by surface model creation and orthophoto production. In contrast to conventional airborne mapping systems, UAS has limited ground coverage due to low flying height and limited flying time, yet it offers an attractive alternative to high performance airborne systems, as the cost of the sensors and platform, and the flight logistics, is relatively low. In addition, UAS is better suited for small area data acquisitions and to acquire data in difficult to access areas, such as urban canyons or densely built-up environments. The main question with respect to the use of UAS is whether the inexpensive consumer sensors installed in UAS platforms can provide the geospatial data quality comparable to that provided by conventional systems.This study aims at the performance evaluation of the current practice of UAS-based topographic mapping by reviewing the practical aspects of sensor configuration, georeferencing and point cloud generation, including comparisons between sensor types and processing tools. The main objective is to provide accuracy characterization and practical information for selecting and using UAS solutions in general mapping applications. The analysis is based on statistical evaluation as well as visual examination of experimental data acquired by a Bergen octocopter with three different image sensor configurations, including a GoPro HERO3+ Black Edition, a Nikon D800 DSLR and a Velodyne HDL-32. In addition, georeferencing data of varying quality were acquired and evaluated. The optical imagery was processed by using three commercial point cloud generation tools. Comparing point clouds created by active and passive sensors by using different quality sensors, and finally, by different commercial software tools, provides essential information for the performance validation of UAS technology.

scholarly journals Raster Vs. Point Cloud LiDAR Data Classification

2014 ◽  
Vol XL-7 ◽  
pp. 79-83 ◽  
Author(s):  
N. El-Ashmawy ◽  
A. Shaker
Keyword(s):  
Land Cover ◽  
Point Cloud ◽  
Image Data ◽  
Data Classification ◽  
Land Cover Classification ◽  
Flying Height ◽  
Lidar Data ◽  
Urban District ◽  
Raster Image ◽  
Point Data

Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the classification results can be achieved by using the proposed approach.

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 General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

2019 ◽  
Vol 11 (23) ◽  
pp. 2737 ◽  
Author(s):  
Minsu Kim ◽  
Seonkyung Park ◽  
Jeffrey Danielson ◽  
Jeffrey Irwin ◽  
Gregory Stensaas ◽  
...  
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Conjugate Point ◽  
Airborne Lidar ◽  
Lidar Data ◽  
General Function ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Uncertainty Model ◽  
External Uncertainty

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals COMPARISON OF UAS-BASED PHOTOGRAMMETRY SOFTWARE FOR 3D POINT CLOUD GENERATION: A SURVEY OVER A HISTORICAL SITE

2017 ◽  
Vol IV-4/W4 ◽  
pp. 55-61 ◽  
Author(s):  
F. Alidoost ◽  
H. Arefi
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Surface Model ◽  
Geospatial Information ◽  
Software Packages ◽  
Comparison Results ◽  
3D City Modelling ◽  
Time Acquisition

Nowadays, Unmanned Aerial System (UAS)-based photogrammetry offers an affordable, fast and effective approach to real-time acquisition of high resolution geospatial information and automatic 3D modelling of objects for numerous applications such as topography mapping, 3D city modelling, orthophoto generation, and cultural heritages preservation. In this paper, the capability of four different state-of-the-art software packages as 3DSurvey, Agisoft Photoscan, Pix4Dmapper Pro and SURE is examined to generate high density point cloud as well as a Digital Surface Model (DSM) over a historical site. The main steps of this study are including: image acquisition, point cloud generation, and accuracy assessment. The overlapping images are first captured using a quadcopter and next are processed by different software to generate point clouds and DSMs. In order to evaluate the accuracy and quality of point clouds and DSMs, both visual and geometric assessments are carry out and the comparison results are reported.

scholarly journals CAMERA CALIBRATION ACCURACY AT DIFFERENT UAV FLYING HEIGHTS

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 595-600 ◽  
Author(s):  
A. R. Yusoff ◽  
M. F. M. Ariff ◽  
K. M. Idris ◽  
Z. Majid ◽  
A. K. Chong
Keyword(s):  
Camera Calibration ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Digital Camera ◽  
Bundle Adjustment ◽  
Digital Cameras ◽  
Mapping Accuracy ◽  
Image Mapping ◽  
Object Distance ◽  
Uav Image

Unmanned Aerial Vehicles (UAVs) can be used to acquire highly accurate data in deformation survey, whereby low-cost digital cameras are commonly used in the UAV mapping. Thus, camera calibration is considered important in obtaining high-accuracy UAV mapping using low-cost digital cameras. The main focus of this study was to calibrate the UAV camera at different camera distances and check the measurement accuracy. The scope of this study included camera calibration in the laboratory and on the field, and the UAV image mapping accuracy assessment used calibration parameters of different camera distances. The camera distances used for the image calibration acquisition and mapping accuracy assessment were 1.5&amp;thinsp;metres in the laboratory, and 15 and 25&amp;thinsp;metres on the field using a Sony NEX6 digital camera. A large calibration field and a portable calibration frame were used as the tools for the camera calibration and for checking the accuracy of the measurement at different camera distances. Bundle adjustment concept was applied in Australis software to perform the camera calibration and accuracy assessment. The results showed that the camera distance at 25&amp;thinsp;metres is the optimum object distance as this is the best accuracy obtained from the laboratory as well as outdoor mapping. In conclusion, the camera calibration at several camera distances should be applied to acquire better accuracy in mapping and the best camera parameter for the UAV image mapping should be selected for highly accurate mapping measurement.

scholarly journals SECTION-BASED TREE SPECIES IDENTIFICATION USING AIRBORNE LIDAR POINT CLOUD

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1001-1007 ◽  
Author(s):  
C. Yao ◽  
X. Zhang ◽  
H. Liu
Keyword(s):  
Species Identification ◽  
Tree Species ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Forest Community ◽  
Surface Model ◽  
Lidar Data ◽  
Palm Tree ◽  
Crown Height ◽  
Individual Tree

The application of LiDAR data in forestry initially focused on mapping forest community, particularly and primarily intended for largescale forest management and planning. Then with the smaller footprint and higher sampling density LiDAR data available, detecting individual tree overstory, estimating crowns parameters and identifying tree species are demonstrated practicable. This paper proposes a section-based protocol of tree species identification taking palm tree as an example. Section-based method is to detect objects through certain profile among different direction, basically along X-axis or Y-axis. And this method improve the utilization of spatial information to generate accurate results. Firstly, separate the tree points from manmade-object points by decision-tree-based rules, and create Crown Height Mode (CHM) by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM). Then calculate and extract key points to locate individual trees, thus estimate specific tree parameters related to species information, such as crown height, crown radius, and cross point etc. Finally, with parameters we are able to identify certain tree species. Comparing to species information measured on ground, the portion correctly identified trees on all plots could reach up to 90.65&amp;thinsp;%. The identification result in this research demonstrate the ability to distinguish palm tree using LiDAR point cloud. Furthermore, with more prior knowledge, section-based method enable the process to classify trees into different classes.

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