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


Harwin, S and Lucieer, A, Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery, Remote Sensing, 4, (6) pp. 1573-1599. ISSN 2072-4292 (2012) [Refereed Article]


Copyright Statement

Licensed under the Creative Commons Attribution 3.0 Unported (CC BY 3.0)

DOI: doi:10.3390/rs4061573


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 (<13 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 2540 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.

Item Details

Item Type:Refereed Article
Research Division:Earth Sciences
Research Group:Physical Geography and Environmental Geoscience
Research Field:Physical Geography and Environmental Geoscience not elsewhere classified
Objective Division:Environment
Objective Group:Ecosystem Assessment and Management
Objective Field:Ecosystem Assessment and Management at Regional or Larger Scales
UTAS Author:Harwin, S (Mr Stephen Harwin)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:79081
Year Published:2012
Web of Science® Times Cited:283
Deposited By:Geography and Environmental Studies
Deposited On:2012-08-16
Last Modified:2013-04-26
Downloads:503 View Download Statistics

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