# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Surface Model Generation
For visual comparison and analysis, subsets of those test sites are presented in this section. Figure 19 shows a subset of the Trento test site, which covers a hospital and its surrounding area of \(360\times 360\) m\({}^{2}\). A Pleiades ortho-image, the corresponding LiDAR DSM, the stereo-derived DSMs as well as the triplet-derived DSM are illustrated. The terrain heights of the DSMs are scaled between 240 m (black) and 300 m (white). Infrastructural changes due to ongoing construction activities since the LiDAR acquisition can be seen. For instance, there is a new helipad and a parking lot, which previously used to be a park. The stereo-derived DSMs, in general, are affected by occlusion areas, which use to increase with increasing stereo intersection angle and which almost vanish in the triplet-derived DSM. The DSM derived from the 1-2 Pleiades stereo pair looks different in comparison to the others and seems to be more similar w.r.t. the LiDAR model. This is due to the small stereo intersection angle, which implies higher image similarity and thus a higher performance of the stereo matching with reduced occlusion areas. However, due to this weak geometric disposition, the height accuracy of this DSM is worse than for the other DSMs. Exemplary quantitative analysis was made through comparison of LiDAR to stereo and triplet-derived DSMs. Due to the large temporal gap between LiDAR and Pleiades data acquisition only selected areas were analyzed which are not affected by temporal change due to construction, vegetation growth or cloud cover. The results are summarized in Table 11. The STD of the height differences confirm the interdependence of stereo intersection angles and 3D mapping accuracy, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Standard Version** & **Accurate Version** \\ \hline \multirow{2}{*}{Matcher} & \multirow{2}{*}{SGM} & \multirow{2}{*}{SGM} \\  & & **Census 9 \(\times\) 9 pixel** \\  & & **and SAD 3 \(\times\) 3 pixel** \\ \multirow{2}{*}{Prediction} & \multirow{2}{*}{SRTM DEM} & \multirow{2}{*}{SRTM DEM} \\  & & \multirow{2}{*}{\(P_{1}=0.4\), \(P_{2}^{{}^{\prime}}=1.5\)} \\  & & 4 \\ \multirow{2}{*}{Pyramid levels} & \multirow{2}{*}{4} & \multirow{2}{*}{4} \\  & & 41/15/15 & **41/31/31** \\ \multirow{2}{*}{Backmatching distance threshold} & \multirow{2}{*}{1.5} & \multirow{2}{*}{1.5} \\  & & & \\ \multirow{2}{*}{Peak filter size} & \multirow{2}{*}{20/25/25} & \multirow{2}{*}{20/25/25} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Main parameters used for stereo matching: Two sets are defined, namely the standard version and the more accurate version. Differences in the parameters are marked in bold face. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **TPs** & \multicolumn{2}{c}{**Before Adjustment [pxl]**} & \multicolumn{2}{c}{**After Adjustment [pxl]**} \\  & & **Mean** & **STD** & **Mean** & **STD** \\ \hline
1–2 & 20 & \(-\)0.29 & 0.58 & \(-\)0.04 & 0.48 \\
2–3 & 20 & \(-\)0.66 & 1.05 & 0.03 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistics on the deviation of tie-points orthogonal to the epipolar direction given in pixels for the Innsbruck East Triplet. with worst accuracy achieved for the 1-2 stereo pair, that was discussed above. The triplet-derived DSM shows similar accuracy like the other stereo-derived DSMs. Nonetheless, it shows the best consistency in comparison to the LiDAR DSM when considering its visual appearance. It shows a clearly reduced amount of occlusion areas and more reliable and improved structures of buildings. Figure 20 shows a subset of the Innsbruck test site, which covers an urban region of 1950 \(\times\) 1000 m\({}^{2}\). It illustrates an ortho-image, a LiDAR DSM, a stereo DSM generated by the commercial software Geomatica 2013 by PCI Geomatics (denoted as 2D matching DSM) and the stereo-derived DSM as generated by our workflow (proposed DSM).