Assessing the Accuracy of High Resolution Digital Surface Models Computed by PhotoScan® and MicMac® in Sub-Optimal Survey Conditions

For monitoring purposes and in the context of geomorphological research, Unmanned Aerial Vehicles (UAV) appear to be a promising solution to provide multi-temporal Digital Surface Models (DSMs) and orthophotographs. There are a variety of photogrammetric software tools available for UAV-based data. The objective of this study is to investigate the level of accuracy that can be achieved using two of these software tools: Agisoft PhotoScan ® Pro and an open-source alternative, IGN © MicMac ® , in sub-optimal survey conditions (rugged terrain, with a large variety of morphological features covering a range of roughness sizes, poor GPS reception). A set of UAV images has been taken by a hexacopter drone above the Riviere des Remparts, a river on Reunion Island. This site was chosen for its challenging survey conditions: the topography of the study area (i) involved constraints on the flight plan; (ii) implied errors on some GPS measurements; (iii) prevented an optimal distribution of the Ground Control Points (GCPs) and; (iv) was very complex to reconstruct. Several image processing tests are performed with different scenarios in order to analyze the sensitivity of each software package to different parameters (image quality, numbers of GCPs, etc.). When computing the horizontal and vertical errors within a control region on a set of ground reference targets, both methods provide rather similar results. A precision up to 3–4 cm is achievable with these software packages. The DSM quality is also assessed over the entire study area comparing PhotoScan DSM and MicMac DSM with a Terrestrial Laser Scanner (TLS) point cloud. PhotoScan and MicMac DSM are also compared at the scale of particular features. Both software packages provide satisfying results: PhotoScan is more straightforward to use but its source code is not open; MicMac is recommended for experimented users as it is more flexible.

[1]  Paul-Henri Faure,et al.  UAV LINEAR PHOTOGRAMMETRY , 2015 .

[2]  J. Brasington,et al.  Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets , 2009 .

[3]  A. Vedaldi An open implementation of the SIFT detector and descriptor , 2007 .

[4]  J. Ryan,et al.  UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet , 2015 .

[5]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  M. Pierrot Deseilligny,et al.  APERO, AN OPEN SOURCE BUNDLE ADJUSMENT SOFTWARE FOR AUTOMATIC CALIBRATION AND ORIENTATION OF SET OF IMAGES , 2012 .

[7]  David Katz,et al.  Technical note: 3D from standard digital photography of human crania-a preliminary assessment. , 2014, American journal of physical anthropology.

[8]  C. Strecha,et al.  The Accuracy of Automatic Photogrammetric Techniques on Ultra-light UAV Imagery , 2012 .

[9]  S. Robson,et al.  Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application , 2012 .

[10]  Marco Dubbini,et al.  Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments , 2013, Remote. Sens..

[11]  C. Strecha,et al.  PHOTOGRAMMETRIC PERFORMANCE OF AN ULTRA LIGHT WEIGHT SWINGLET UAV , 2012 .

[12]  T. Van Damme,et al.  Computer Vision Photogrammetry for Underwater Archaeological Site Recording in a Low-Visibility Environment , 2015 .

[13]  Pierre Grussenmeyer,et al.  The use of small‐format and low‐altitude aerial photos for the realization of high‐resolution DEMs in mountainous areas: application to the Super‐Sauze earthflow (Alpes‐de‐Haute‐Provence, France) , 2002 .

[14]  Juha Suomalainen,et al.  High-Res Digital Surface Modeling using Fixed-Wing UAV-based Photogrammetry , 2013 .

[15]  S. Robson,et al.  Mitigating systematic error in topographic models derived from UAV and ground‐based image networks , 2014 .

[16]  J. Brasington,et al.  Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry , 2014 .

[17]  F. Neitzel,et al.  Mobile 3d Mapping with a Low-Cost Uav System , 2012 .

[18]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[19]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[20]  F. Visser,et al.  Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry , 2015 .

[21]  Christophe Delacourt,et al.  DRELIO: An unmanned helicopter for imaging coastal areas , 2009 .

[22]  D. Milan,et al.  Influence of survey strategy and interpolation model on DEM quality , 2009 .

[23]  K. Moffett,et al.  Remote Sens , 2015 .

[24]  Heikki Saari,et al.  Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture , 2013, Remote. Sens..

[25]  Mark A. Fonstad,et al.  Topographic structure from motion: a new development in photogrammetric measurement , 2013 .

[26]  Arko Lucieer,et al.  Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..