Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry

Toby Nicholas Tonkin; N.G. Midgley

doi:10.3390/rs8090786

Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry

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Abstract

The use of small UAV (Unmanned Aerial Vehicle) and Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) for acquiring survey datasets is now commonplace, however, aspects of the SfM-MVS workflow require further validation. This work aims to provide guidance for scientists seeking to adopt this aerial survey method by investigating aerial survey data quality in relation to the application of ground control points (GCPs) at a site of undulating topography (Ennerdale, Lake District, UK). Sixteen digital surface models (DSMs) were produced from a UAV survey using a varying number of GCPs (3-101). These DSMs were compared to 530 dGPS spot heights to calculate vertical error. All DSMs produced reasonable surface reconstructions (vertical root-mean-square-error (RMSE) of

Original language	English
Article number	786
Journal	Remote Sensing
Volume	8
Issue number	9
DOIs	https://doi.org/10.3390/rs8090786
Publication status	Published - 21 Sept 2016
Externally published	Yes

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Tonkin_Midgley_RS_2016_Ground-Control_Networks_for_Image_Based_Surface_ReconstructionFinal published version, 6.19 MBLicence: CC BY

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Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry

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