Extended Abstract
Introduction
Nowadays, UAV photogrammetry has become one of the most effective methods of collecting spatial data according to the factors time, cost, quality and variety of outputs among terrestrial and aerial mapping technologies. Because the quality of a UAV photogrammetry products depends on the network design parameters setting according to the existing conditions and limitations, therefore, awareness of the behavior and impact of network design parameters on the quality of 3D reconstruction to achieve optimal quality of outputs is a very important issue. However, due to the time-consuming and the high cost of doing this study with huge real data, comprehensive research has not yet been conducted to measure the behavior of the effective parameters in network design and 3D reconstruction. There are various parameters include camera field of view, positioning error and imaging tilt in flight navigation, flight altitude and designed ground pixel dimensions, amount of sidelap and overlap images, image observation noise due to image quality, aerial triangulation error, in the process of preparing the map from aerial images, which is known as the most important parameters of UAV photogrammetric network design. In this paper, the simulation method is used to investigate the effect and behavior of the above parameters on the quality of three-dimensional reconstruction.
 
Materials & Methods
In the proposed method in MATLAB software environment, from a point with known 3D coordinates, using the collinearity equations and the value set for the network design parameters and their standard deviation according to the reality and experience of the expert, the imaging is done in a simulated manner. Then, by applying random and systematic errors on the visual observations and aerial triangulation parameters, the collinearity equations of the photographic observations form the desired point and using the least squares method of error in solving nonlinear equations, three-dimensional reconstruction, and quality are performed, then it has been evaluated by the Monte Carlo method. To achieve the results with high reliability, the quality of three-dimensional reconstruction is evaluated in five modes, respectively, ideal, excellent, good, average and bad, according to the expert opinion in setting the values of each parameter.
Results & Discussion
The results of this study show, most effective parameters in the quality of three-dimensional reconstruction in ideal conditions are camera instability, error of exterior orientation parameters and image quality, respectively, which gradually give way to parameters of flight altitude, imaging coverage and camera field of view in bad conditions. The results of the flight navigation error show, increased imaging platform instability has no significant effect on the average accuracy of 3D reconstruction, however, the accuracy changes in different places increase up to 20% due to the heterogeneity of the coverage and the visibility of different parts of the earth in the video network. The results also show that with increasing geometric instability of the non-metric camera, the accuracy of 3D reconstruction decreases linearly, in this regard, the imaging in bad conditions and the quality of the camera, the slower the reduction speed. It has also been shown that with increasing image observation error, which depends on image quality, the accuracy of 3D reconstruction decreases linearly. The results of the study of aerial triangulation parameters show that the three-dimensional reconstruction error increases linearly with increasing tie point matching error. In addition, as the focal length increases in the fixed flight altitude mode, the horizontal accuracy increases in proportion to the inverse magnification, and as the focal length decreases, the altitude accuracy decreases linearly, in the fixed ground sampling distance (GSD) mode, the horizontal error of 3D reconstruction is slowly reduced to 20%, while the height error increases with increasing height and decreasing the geometric resistance of the network by a factor of half magnification. The results also show that unlike traditional photogrammetry here, with increasing flight altitude, the horizontal and altitude errors of the 3D reconstruction increase linearly. The results of the study of the parameters of sidelap and overlap images show that the sidelap and overlap images can change the surface error up to 10 times and the height error and complete three-dimensional reconstruction up to 5 times.
 
Conclusion
This study, while introducing the effective parameters in three-dimensional reconstruction by UAV photogrammetric method, has investigated the behavior and effect of these parameters on the quality of three-dimensional reconstruction in the simulation environment. This means how the quality of the reconstruction changes with minor changes to each of the parameters from half to twice the standard mode. Therefore, the closer this simulation is to reality, the more practical the results will be. Naturally, this complicates the simulation and increases the computational volume. Although this simulation is not entirely consistent with the actual situation, it can provide a kind of behavioral measurement of the parameters that serves as a complementary research to routine try and error investigations.