Abstract—The growth and development of Unmanned Aerial Vehicles (UAVs)
as a photogrammetric platform, concurrently with the advances in Computer
Vision (CV) and image processing algorithms have resulted using UAV
Photogrammetry in several topographic applications. CV software algorithms
rely on extracting, describing, and matching tie points from the sequences
overlapping images to generate 3D colored point clouds. One of the biggest
problems obstructing the automated processing of UAV imagery is the featureless
of the covered surface. This paper has provided the ability, results, and accuracy
of processing images captured by UAVs over non-textured sandy surfaces by
providing four aligning and geo-referencing techniques. These four methods,
IG/blind matching, IG/reference matching, DG/blind matching, and
DG/reference matching, have been presented and tested for 630 aerial images with
80 % overlap and 80 % side lap covered approximately 1 km2 at altitude 178 m
above ground level (AGL). The results showed that the captured images could be
used to extract the photogrammetric topographical measurements with reliable
accuracy. The four techniques' geometric accuracy has ranged between (0.043 m
to 0.076 m) & (0.047 m to 0.074) for generated point clouds and linear exterior
orientation (EO) parameters, respectively. The indirect geo-referencing with
reference matching (IG/reference) recorded the highest-level accuracy of point
clouds with 0.043m RMSE compared to the direct geo-referencing with reference
matching (DG/reference) which gave the highest geometric accuracy of the linear EO parameters with 0.047m |
