Volume IV-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 255-262, 2018
https://doi.org/10.5194/isprs-annals-IV-2-255-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 255-262, 2018
https://doi.org/10.5194/isprs-annals-IV-2-255-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  28 May 2018

28 May 2018

VEGETATION REMOVAL FROM UAV DERIVED DSMS, USING COMBINATION OF RGB AND NIR IMAGERY

D. Skarlatos and M. Vlachos D. Skarlatos and M. Vlachos
  • Cyprus University of Technology, Dep. of Civil Engineering and Geomatics, P.O.Box 50329, Limassol 3603, Cyprus

Keywords: DSM, DTM, near infrared, vegetation removal

Abstract. Current advancements on photogrammetric software along with affordability and wide spreading of Unmanned Aerial Vehicles (UAV), allow for rapid, timely and accurate 3D modelling and mapping of small to medium sized areas. Although the importance and applications of large format aerial overlaps cameras and photographs in Digital Surface Model (DSM) production and LIDAR data is well documented in literature, this is not the case for UAV photography. Additionally, the main disadvantage of photogrammetry is the inability to map the dead ground (terrain), when we deal with areas that include vegetation. This paper assesses the use of near-infrared imagery captured by small UAV platforms to automatically remove vegetation from Digital Surface Models (DSMs) and obtain a Digital Terrain Model (DTM). Two areas were tested, based on the availability of ground reference points, both under trees and among vegetation, as well as on terrain. In addition, RGB and near-infrared UAV photography was captured and processed using Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms to generate DSMs and corresponding colour and NIR orthoimages with 0.2 m and 0.25 m as pixel size respectively for the two test sites. Moreover, orthophotos were used to eliminate the vegetation from the DSMs using NDVI index, thresholding and masking. Following that, different interpolation algorithms, according to the test sites, were applied to fill in the gaps and created DTMs. Finally, a statistic analysis was made using reference terrain points captured on field, both on dead ground and under vegetation to evaluate the accuracy of the whole process and assess the overall accuracy of the derived DTMs in contrast with the DSMs.