# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Surface Model Generation
As discussed in Section 4.2.6 those outliers lead to non-robust mean and STD estimates. The MAE measure also suffers from outliers and cannot contribute to the interpretation. Next, the plots reveal that four distributions are significantly worse (i.e., larger STD and nmAD) than all others. Those pairs are 1-3, 2-4, 3-5 and 4-6. Those pairs are exactly the four pure across track stereo pairs (cf. Figure 6) with small convergence angles. Due to the outliers, those pairs cannot be detected reliably with the STD estimate but with the nmAD. Two of these distributions show a bias in their mean value, that is, pairs 1-3 and 3-5. Again this behavior is not mapped to the mean values but to the MED values. This just emphasizes that non-robust fitting of a normal distribution based on mean and STD is not the best idea for our DSM difference models as they are non-Gaussian distributed. \begin{table}
\begin{tabular}{r r r r r r r r r} \hline \hline
**Model** & **Min** & **Max** & **Mean** & **STD** & **MED** & **NMAD** & **MAE** & **nodata** & \(\delta\) \\  & **[m]** & **[m]** & **[m]** & **[m]** & **[m]** & **[m]** & **[m]** & **[\%]** & **[\%]** \\ \hline
1–2 & \(-895.14\) & \(769.21\) & \(-0.36\) & **4.33** & 0.06 & 0.97 & 2.05 & 14.34 & 25.2 \\
1–3 & \(-895.47\) & \(627.51\) & \(-1.06\) & 4.47 & \(-0.49\) & 1.46 & 2.33 & **6.60** & 12.2 \\
1–4 & \(-893.79\) & \(627.86\) & \(-0.39\) & 4.51 & 0.07 & 0.94 & **2.03** & 18.77 & 27.5 \\
1–5 & \(-891.36\) & \(630.43\) & \(-0.53\) & 4.48 & **0.03** & 1.20 & 2.22 & 18.07 & 24.0 \\
1–6 & \(-894.05\) & \(\mathbf{626.80}\) & \(-0.47\) & 5.48 & \(-0.24\) & **0.86** & 2.25 & 27.55 & 33.9 \\
2–3 & \(-895.65\) & \(628.78\) & \(-0.26\) & 4.47 & 0.07 & 1.04 & 2.15 & 16.76 & 26.4 \\
2–4 & \(-896.24\) & \(632.41\) & \(-0.40\) & 4.47 & 0.08 & 1.46 & 2.26 & 7.76 & 12.5 \\
2–5 & \(-893.53\) & \(628.38\) & 0.09 & 5.39 & 0.28 & 0.87 & 2.36 & 28.00 & 37.6 \\
2–6 & \(-382.89\) & \(627.91\) & \(-0.17\) & 4.90 & **0.03** & 1.14 & 2.38 & 22.77 & 25.0 \\
3–4 & \(-895.04\) & \(627.53\) & \(-0.06\) & 4.49 & 0.22 & 1.01 & 2.13 & 14.82 & 22.77 \\
3–5 & \(-381.51\) & \(629.00\) & \(-0.09\) & 4.56 & 0.43 & 1.58 & 2.44 & 10.72 & 13.0 \\
3–6 & \(-894.66\) & \(627.40\) & \(-0.61\) & 4.53 & \(-0.24\) & 1.04 & 2.13 & 19.06 & 24.7 \\
4–5 & \(-894.42\) & \(628.14\) & 0.19 & 4.90 & 0.37 & 0.93 & 2.30 & 25.05 & 30.3 \\
4–6 & \(-897.25\) & \(630.22\) & \(-0.66\) & 4.75 & \(-0.23\) & 1.69 & 2.52 & 12.28 & 12.6 \\
5–6 & \(-893.14\) & \(766.04\) & \(-0.48\) & 4.61 & \(-0.08\) & 0.92 & 2.09 & 21.22 & 26.2 \\ \hline
1–2 \& \(3\)–4 & \(-895.10\) & 628.31 & \(-0.20\) & 4.41 & 0.15 & 0.91 & 2.07 & 12.21 & 25.2 \& 22.7 \\
1–2 \& \(3\)–4 \& \(5\)–6 & \(-894.58\) & 627.59 & \(-0.29\) & 4.37 & 0.08 & 0.84 & 2.01 & 12.85 & 25.2 \& 22.7 \& 26.2 \\ \(\delta\)\(\in[0,20]\) & \(-896.42\) & 626.71 & \(-0.51\) & 4.07 & \(-\mathbf{0.01}\) & 1.14 & 1.98 & 11.07 & various \\ \(\delta\in[0,30]\) & \(-894.17\) & 627.92 & \(-0.37\) & 4.56 & 0.05 & 0.93 & 2.16 & 7.30 & various \\ \(\delta\in[30,40]\) & \(-\mathbf{893.98}\) & \(\mathbf{575.56}\) & \(\mathbf{-0.05}\) & **2.68** & 0.16 & **0.41** & **1.00** & 55.19 & various \\ all & \(-894.63\) & \(765.43\) & \(-0.46\) & 4.74 & 0.05 & 1.09 & 2.32 & **1.12** & all \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Accuracy analysis of the resulting DSMs in comparison to LiDAR reference data (with \(\delta\) being the convergence angle). Best results are shown in bold face for single stereo and for multi stereo sets. As discussed above, it can be observed that small intersection angles lead to large DSM completeness (i.e., low nodata percentage) where the 1-3 pair performs best. However, despite the completeness, this pair is rather inaccurate with one of the largest nmAD values. This behavior can be traced back to the small intersection angle, such that the stereo images are more similar and thus, image matching results in a more complete disparity map. However, due to the small intersection angle, the spatial forward intersection yields a larger 3D error. Same holds, for instance, for the 2-4 pair.