Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.
Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery
2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
LINE EXTRACTION FOR ARTIFICIAL OBJECTS USING UNMANNED AERIAL VEHICLE IMAGERY
British Library Conference Proceedings | 2016
|VEHICLE POSITION ESTIMATION WITH AERIAL IMAGERY FROM UNMANNED AERIAL VEHICLES
British Library Conference Proceedings | 2020
|Unmanned aerial vehicle infrared visible light camera mount and unmanned aerial vehicle
Europäisches Patentamt | 2021
|