## 6 Discussion  
For indirect georeferencing, the most reliable strategy to guarantee the DEM quality consists in installing targets all along the study area. However, when it is not possible to achieve an optimal GCPs distribution, some strategies enable us to limit distortion effects. Figure 9: Comparison of the error (estimated using the check points) obtained with different processing chains for each flight. The error on each check points is represented as a function of the distance to the (MN) profile at the extremity of the control region. This study highlights the fact that, in case of poor GCPs distribution, a good match between the flight plan strategy and the choice of camera distortion model is critical to limit bowl effects. Some of the observed differences between PhotoScan and MicMac reconstructions may be due to the step of tie point automatic detection, which is based on C++ SIFT algorithm [32] in Micmac [31] while PhotoScan claims to achieve higher image matching quality using custom algorithms that are similar to SIFT [3]. As mentioned in Section 2.4, the degrees of freedom in bundle adjustment depend on the camera model (for example, 8 degrees of freedom for the [PERSON]'s model used with PhotoScan(r) and 12 degrees of freedom for the [PERSON]'s model used with MicMac(r)). As already mentioned, variation can also be due to differences in the filtering algorithm or in the minimization algorithm during the step of bundle adjustment. For MicMac(r), this step is well described in reference [15], but for PhotoScan(r), it is a \"black-box\". That stresses the need for detailed information about the algorithms and parameters used in SfM processing software. The fact that MicMac \"F15P7\" gives very poor results for oblique pointing camera (Flight 2) is consistent with reference [15], which mentions that in certain conditions, oblique imagery can reduce the quality of the results obtained using the \"F15P7\" distortion model. They hypothesize that the determination of many additional parameters can lead to over-parametrization of the least-square estimation and they advise in such cases to use a physical camera model. Our results are in agreement with this hypothesis since, for the same dataset, MicMac--Fraser provides the best results, with a RMS error of 25.9 cm. More generally, oblique pointing camera or change in flight altitude contribute to diversifying viewing angles throughout the whole survey. A point is therefore seen on several images with very variable viewing angles, which can make the tie points detection more difficult but seems to increase the quality of the reconstruction, limiting the uncertainty in camera parameters assessment. To improve the quality of the resulting DEM, one could also consider:  
* combining Flight 2 and Flight 3 scenarios, i.e., different altitude of flight with an oblique pointing camera, with perhaps even better results;
* combining Flight 2 or Flight 3 scenario with other optical camera models or processing strategies. Optimizing the optical camera model and/or the processing strategy could also improve the quality of the resulting DEM. As an example, [18] have identified that with PhotoScan(r), a camera model parametrization using focal length (f), principal point offset (cx, cy), radial distortions (K1, K2, K3) and tangential distortions (P1, P2) (default configuration in PhotoScan(r)) is more efficient than adding skew coefficients to this parameters set. About the choice of camera distortion model, as mentioned in reference [34], using additional parameters introduces new unknowns into the bundle-adjustment procedure and an improper use of these parameters can adversely affect the determinability of all system parameters in the procedure of self-calibration. Moreover, it is known that the precision of tie points can contribute to reduce geometric distortion.