The improvement of unmanned aerial system and photogrammetric computer vision (CV) algorithms has presented an aerial imaging technique for high accuracy and low-cost alternatives for mapping and topographic applications. Structure from motion (SFM) is an automation photogrammetric CV algorithm used for generating 3D coloured point clouds and 3D models from overlapping images. One of the biggest problems preventing the automation extraction and matching key points in the aligning aerial images is the non-texture of the covered area surface. This paper assessed the effect of flight altitude and overlap degree on 3D point clouds’ geometric accuracy and models produced by unmanned aerial vehicle (UAV) images captured over non-textured sandy areas. Four flight altitudes (140, 160, 180 and 200 m) related to spatial resolution (3.41, 3.9, 4.39 and 4.68 cm/pix ground sample distance (GSD)), respectively, and three overlap levels (60%, 70% and 80%) were assessed using RGB images captured by UX5 UAV over a non-textured sandy area in Jahra, Kuwait. The results showed that altitude increment might reduce flight time, processing time and cost, keeping with the acceptable and suitable geometric accuracy. Generally, favourable results are obtained for the four altitudes and overlap degrees of 80% at least. Multivariate nonlinear regression analysis was used to fit the relation between geometric accuracy, image overlap and GSD cm/pixel for the seven missions determining two formulas that predict the geometrical accuracy of the UAV point cloud with a precision of 92.76% and 91.91% for both formulas. |
