Accuracy assessment of coastal topography derived from uav images

Abstract : To monitor coastal environments, Unmanned Aerial Vehicle (UAV) is a low-cost and easy to use solution to enable data acquisition with high temporal frequency and spatial resolution. Compared to Light Detection And Ranging (LiDAR) or Terrestrial Laser Scanning (TLS), this solution produces Digital Surface Model (DSM) with a similar accuracy. To evaluate the DSM accuracy on a coastal environment, a campaign was carried out with a flying wing (eBee) combined with a digital camera. Using the Photoscan software and the photogrammetry process (Structure From Motion algorithm), a DSM and an orthomosaic were produced. Compared to GNSS surveys, the DSM accuracy is estimated. Two parameters are tested: The influence of the methodology (number and distribution of Ground Control Points, GCPs) and the influence of spatial image resolution (4.6 cm vs 2 cm). The results show that this solution is able to reproduce the topography of a coastal area with a high vertical accuracy (< 10 cm). The georeferencing of the DSM require a homogeneous distribution and a large number of GCPs. The accuracy is correlated with the number of GCPs (use 19 GCPs instead of 10 allows to reduce the difference of 4 cm); the required accuracy should be dependant of the research problematic. Last, in this particular environment, the presence of very small water surfaces on the sand bank does not allow to improve the accuracy when the spatial resolution of images is decreased.
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 〈10.5194/isprsarchives-XLI-B1-1127-2016〉
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https://hal.archives-ouvertes.fr/hal-01453451
Contributeur : Xavier Bertin <>
Soumis le : jeudi 2 février 2017 - 18:02:44
Dernière modification le : jeudi 11 janvier 2018 - 06:23:35

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Xavier Bertin, Nathalie Long, Bastien Millescamps, Nicolas Lachaussée, Antoine Dumont, et al.. Accuracy assessment of coastal topography derived from uav images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 〈10.5194/isprsarchives-XLI-B1-1127-2016〉. 〈hal-01453451〉

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