Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery

By Steve Harwin, Arko Lucieer. Remote Sensing, 2012

669 citations recorded by the source corpus.

Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from ~50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored.

Subfields: Environmental Engineering, Geology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/0973e3ddaf9742df6dcebc29580e950675b94d3c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Geology

In Ocean Engineering

Citations in the corpus

Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy (2017, methodology): “Harwin and Lucier [45] used 21, 10, and six ground control points and reported RMSExyz of 3.”
Unmanned aerial systems for photogrammetry and remote sensing: A review (2014, background): “Harwin and Lucieer (2012) present a comprehensive compilation of the state-of-the-art techniques and their results for UASbased point cloud generation, and present an accuracy study for the multi-view stereopsis technique.”
Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging (2017, background): “UAVs for remote sensing are increasingly being deployed for low-cost, on-demand aerial photography and photogrammetry applications (Harwin and Lucieer 2012; Rango et al. 2009).”
Accuracy of Unmanned Aerial Vehicle (UAV) and SfM Photogrammetry Survey as a Function of the Number and Location of Ground Control Points Used (2018, background): “Reference [13] uses different subsets of GCPs suggesting that the number of GCPs is an important parameters in accuracy results.”
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) (2015, methodology): “Immerzeel et al. [14], Lucieer et al. [11], and Martha et al. [8] employed a technique to validate the co-registration of multi-temporal DEMs by analyzing areas of the DEMs that were not subject to change.”
Structure from motion photogrammetry in physical geography (2016, methodology): “A large number of studies apply SfM-MVS to coastal environments (Harwin and Lucieer, 2012; James and Quinton, 2014; Mancini et al., 2013; Ružić et al., 2014) to extract the topography of beaches, dunes or cliffs.”
High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision (2013, methodology): “UAS are increasingly being deployed for low-cost, on-demand aerial photography and photogrammetry applications (Harwin & Lucieer, 2012; Hunt et al., 2010; Rango et al., 2009).”
Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure (2015, methodology): “Other studies have georeferenced SFM datasets through the use of ground control points (GCPs) that are visible in images and for which the real world coordinates are measured with high accuracy instruments like differential GPS, RTK-GPS (real-time kinematic GPS), and total station surveying equipmen”
Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform (2019, methodology): “GCPs are used to control the geometric accuracy of images [35].”