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期刊名称: Remote Sensing
Volume:4    Issue:6        Page:1573-1599
ISSN:2072-4292

Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from Unmanned Aerial Vehicle (UAV) imagery期刊论文

作者: Harwin Steve Lucieer Arko
DOI:10.3390/rs4061573

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页码: 1573-1599
出版者: MDPI AG
期刊名称: Remote Sensing
ISSN: 2072-4292
卷期: Volume:4    Issue:6
语言: English
摘要:   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.
相关主题: 3D point cloud, Accuracy assessment, Coastal environment, UAV, Multi-view stereopsis, multi-view stereopsis, accuracy assessment, coastal environment,

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