# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Sensor Modeling and Parameter Optimization
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Type**} & \multicolumn{4}{c}{**Initial [pxl]**} & \multicolumn{4}{c}{**Adjusted [pxl]**} \\  & & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**STD**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**STD**} \\  & & **Across** & **Along** & **Across** & **Along** & **Across** & **Along** & **Across** & **Along** \\ \hline
1 & GCP & 1.68 & \(-\)0.10 & 0.68 & 0.67 & 0.05 & 0.00 & 0.42 & 0.51 \\  & ICP & 1.56 & 0.02 & 0.92 & 0.61 & \(-\)0.23 & 0.09 & 0.68 & 0.72 \\ \hline
2 & GCP & 2.85 & 2.38 & 0.85 & 0.54 & 0.08 & 0.07 & 0.40 & 0.46 \\  & ICP & 2.49 & 2.68 & 1.22 & 0.77 & \(-\)0.31 & 0.26 & 0.88 & 0.73 \\ \hline
3 & GCP & 1.54 & \(-\)0.64 & 0.69 & 0.51 & 0.04 & \(-\)0.02 & 0.45 & 0.42 \\  & ICP & 1.20 & \(-\)0.63 & 0.87 & 0.63 & \(-\)0.31 & 0.01 & 0.63 & 0.66 \\ \hline
4 & GCP & 1.61 & 1.60 & 0.95 & 0.82 & 0.04 & 0.04 & 0.48 & 0.67 \\  & ICP & 1.14 & 1.91 & 1.29 & 0.63 & \(-\)0.42 & 0.31 & 0.87 & 0.74 \\ \hline
5 & GCP & \(-\)2.87 & 11.72 & 0.49 & 0.42 & \(-\)0.09 & 0.32 & 0.49 & 0.37 \\  & ICP & \(-\)3.38 & 11.56 & 0.68 & 0.76 & \(-\)0.60 & 0.18 & 0.68 & 0.68 \\ \hline
6 & GCP & \(-\)2.82 & 12.13 & 0.47 & 0.56 & \(-\)0.09 & 0.33 & 0.47 & 0.46 \\  & ICP & \(-\)3.11 & 12.14 & 0.62 & 0.99 & \(-\)0.37 & 0.27 & 0.59 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 2D geo-location accuracy analysis for all six images of the Ljubljana test site given in pixels. For each image, the first row shows the residual errors based on GCPs and the second row the residual errors for the ICPs. After adjustment the mean STDs are 0.45 pixels for GCPs and 0.73 pixels for ICPs. Figure 17: The 3D error is plotted versus the convergence angle, which are in the range from 4\({}^{\circ}\) to 35\({}^{\circ}\), assuming an 2D location measurement error of 1/8, 1/4 and 1/2 m. On top of that the RMS values of the adjusted lengths (cf. Table 6) are shown in blue. It is clearly visible that the two stereo pairs with small intersection angle (i.e., Innsbruck triplet center 1-3 with 4.6\({}^{\circ}\) and Trento triplet 1-2 with 5.5\({}^{\circ}\)) yield significant worse 3D geo-locations than all other stereo (and tri-stereo) constellations. For constellations involving larger stereo intersection angles the RMS values in planimetry are in a range of 0.2 m to 0.5 m. The RMS values in height are between 0.30 m and 1.5 m. The variations in height are diverse for the given test sites, which can be traced back to the accuracy of the reference data. Here, the best results are achieved for the Ljubljana test site, where highly accurate reference data is available such that the RMS in height also gets down to 0.3 m. In contrast, the Singapore test site holds rounded height reference values and thus this test site shows the highest RMS in height with 1.5 m. Overall, the sensor model adjustment results in optimized models that can be used to derive 3D information with high quality, that is, 0.3 m in planimetry and height, if the reference GCPs are also in this accuracy range. Analog to the 2D accuracy assessment, the 3D mapping accuracy is foremost analyzed for two test sites based on GCPs and ICPs. Then, it is evaluated w.r.t. LiDAR reference data. First, the Innsbruck stereo set and second the Ljubljana multi-view stereo set are analyzed. For the Innsbruck test site, tests with respect to the utilization of a different number of GCPs and ICPs as well as with respect to the comparison of constant versus constant and linear nominator coefficients optimization are performed. The results of this analysis, including mean and standard deviation values of checkpoint residuals, are summarized in Table 7. Again, the utilization of a minimum number of GCPs yields systematic geo-location errors in East, North and height as manifested through the corresponding mean residual values.