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 |
