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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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    Authors
    Tonkin, Toby N. cc
    Midgley, Nicholas G.
    Affiliation
    University of Derby
    Nottingham Trent University
    Issue Date
    2016-09-21
    
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    Abstract
    The use of small UAVs (Unmanned Aerial Vehicles) 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 <0.2 m), however, an improvement in DSM quality was found where four or more GCPs (up to 101 GCPs) were applied, with errors falling to within the suggested point quality range of the survey equipment used for GCP acquisition (e.g., vertical RMSE of <0.09 m). The influence of a poor GCP distribution was also investigated by producing a DSM using an evenly distributed network of GCPs, and comparing it to a DSM produced using a clustered network of GCPs. The results accord with existing findings, where vertical error was found to increase with distance from the GCP cluster. Specifically vertical error and distance to the nearest GCP followed a strong polynomial trend (R2 = 0.792). These findings contribute to our understanding of the sources of error when conducting a UAV-SfM survey and provide guidance on the collection of GCPs. Evidence-driven UAV-SfM survey designs are essential for practitioners seeking reproducible, high quality topographic datasets for detecting surface change.
    Citation
    Tonkin, T. N. and Midgley, N. G. (2016) 'Ground-control networks for image based surface reconstruction: An investigation of optimum survey designs using UAV derived imagery and structure-from-motion photogrammetry', Remote Sensing, 8(9), 786; doi:10.3390/rs8090786
    Publisher
    Multidisciplinary Digital Publishing Institute (MDPI)
    Journal
    Remote Sensing
    URI
    http://hdl.handle.net/10545/621283
    Additional Links
    http://www.mdpi.com/2072-4292/8/9/786
    Type
    Article
    Language
    en
    ISSN
    20724292
    Collections
    Environmental Sustainability Research Centre

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