# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Surface Model Generation  
Before explaining the resulting DSM quality a very interesting point about epipolarity is discussed, which is neglected in other literature. As explained a high-quality epipolar rectification is a pre-requisite for 1D image matching, like the semi-global matching. However, some may think that a Pleiades stereo pair is consistent even without sensor model adjustment. Thus, it may be sufficient to extract a DSM before adjustment and later shift this DSM to some reference system (cf. Reference [5]). Therefore, a test is conducted to determine if the initial sensor models are accurate enough to get epipolar images. A number of TPs is measured in the original images and projected to the epipolar images. There, the distance orthogonal to the epipolar direction (i.e., the column direction) is gathered, which should be zero in the optimal case. Table 9 shows the statistics of the deviation of tie-points orthogonal to the related epipolar direction before and after sensor model adjustment for the Innsbruck East Triplet. While the STD values only reduce a bit, a significant change in the mean values is observed. Especially in the pair 2-3 the initial mean value is \(-0.66\) pixel, thus more than a half-pixel off. It is known \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Type** & **GCPs** & **ICPs** & \multicolumn{3}{c}{**Mean Adjusted [m]**} & \multicolumn{3}{c}{**RMS Adjusted [m]**} \\  & & & **E** & **N** & **H** & **E** & **N** & **H** \\ \hline constant & 1 & 29 & \(-0.79\) & \(-0.07\) & \(-0.93\) & 0.92 & 0.39 & 1.13 \\ terms & 10 & 20 & 0.10 & \(-0.20\) & \(-0.33\) & 0.50 & 0.45 & 0.70 \\  & 30 & 30 \({}^{\dagger}\) & 0.00 & 0.00 & 0.00 & 0.48 & 0.38 & 0.65 \\ \hline constant & 4 & 26 & 1.25 & \(-0.68\) & \(-0.16\) & 1.41 & 0.88 & 0.72 \\ and linear & 10 & 20 & \(-0.02\) & \(-0.30\) & \(-0.21\) & 0.46 & 0.48 & 0.75 \\ terms & 30 & 30 \({}^{\dagger}\) & 0.00 & 0.00 & 0.00 & 0.40 & 0.30 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: 3D geo-location accuracy analysis using different adjustment settings for the Innsbruck stereo set (\({}^{\dagger}\) ICPs equal GCPs). Values are given for East (E), North (N) and Height (H). Figure 18: The 3D length discrepancies for the Ljubljana set sorted from lowest to highest ICP error. ICP values are given in red and corresponding GCP values in blue. that 1D matching degrades drastically if the correct match is not located in the current 1D search space. Overall, this is a proof that sensor models should be adjusted before further 3D processing. In cases where no GCPs are available the sensor model adjustment should be performed with TPs (cf. Reference [5]). Each TP between two images yield four additional equations in the non-linear equation system (cf. Section 4.1) but also three additional unknowns (i.e., the 3D location of that point). TPs can be automatically derived for such stereo images by, for instance, using the _scale invariant feature transform (SIFT)_[127], _speeded up robust features (SURF)_[128] or similar techniques [129]. In this case, a block adjustment can be performed for multiple images and thus for multiple RPC sensor models simultaneously.