# Mapping with Pleiades--End-to-End Workflow
## 6 Discussion
### Main Findings  
From the methodological point of view, we stated that the presented RPC sensor model adjustment in object-space is simple and works well. In contrast to image-space based methods, no additional information like the coefficients of the affine transformation has to be stored. Furthermore, height dependency is an intrinsic part of the adjustment. Epipolar rectification is solved, the presented solution is very general and can also be used for SAR geometries. Several extensions in image matching were presented, mainly for speedup reasons. Details on the adaptive penalty paradigm were presented together with an extension to use truncated disparity space images (again for speedup). For better robustness, the concept of top-level predictions was introduced, which is based on a coarse input DSM, if existing. Subpixel refinement was discussed and a simple solution was described that yields uniform distributed subpixel fraction distribution. Storage concepts for 3D point clouds and their resampling to a regular grid were discussed as well. For DSM fusion a simple method was enhanced to also allow downsampling in the same step. Statistical measures, in particular, the MED and nmAD, were discussed and insights were given why the mean and the STD should not be used to compare DSMs. Regarding DTM generation a novel method was proposed and described in detail. In comparison to its initial publication, it was also extended. The method itself sticks out with its simplicity while yielding good results.  
Also, the evaluation of all 24 Pleiades images revealed exciting insights. As expected the initial 2D and thus also 3D geo-location accuracy is inadequate for mapping. It also has been shown that even one along track stereo pair may suffer from relative geometric distortions, which was demonstratedas deviation orthogonal to the epipolar direction. After adjustment, the residual errors dropped significantly. Here, we observed that the lower limit comes mainly from the quality of the reference GCPs and the image measurements. For the Ljubljana multi-view set, where highly precise reference information was available, a 2D and 3D accuracy was achieved as never achieved before (3D accuracies in the range of 0.2 m to 0.3 m in planimetry and 0.2 m to 0.4 m in height w.r.t. GCPs). This can be seen as empirical proof that the proposed sensor model adjustment and spatial forward intersection procedure perform as desired. During the analysis, we detected an accuracy drop within the across track stereo pairs of the Ljubljana test site, which can be traced back to the small convergence angles of those stereo pairs. Since such Pleiades multi-view constellations were never before processed they were also not reported in literature. One specific outcome of the study on the multiple view data set was that convergence angles in the range of 20\({}^{\circ}\) to 30\({}^{\circ}\), which correspond to a B/H ratio of 0.35 to 0.55 yield best results. It seems that this convergence angle range is the optimal tradeoff in quality between image matching and spatial point intersection. Thus, in cases where many images exist, only stereo pairs with appropriate convergence angle should be used. The comparisons of LiDAR to Pleiades based DSMs showed highly accurate reconstructions, with an nmAD around 0.9 m. It also revealed that robust estimates for characterizing a Gaussian distribution have to be used in remote sensing to avoid incorrect biases when comparing DSMs. Using multiple stereo pairs for DSM generation increased the completeness and the accuracy. Terrain model extraction worked well and introduced no additional errors on the height values. Problems occur in large forests where no ground height value is visible. This aspect may degrade DTMs especially in forested hilly terrains.  
Overall, the presented end-to-end workflow delivers mapping products of very high quality.