## 5 Results  
### Sensor Orientation  
The accuracy results of the direct geopositioning approach computed in object space for each of the four Deimos-2 PAN images are shown in Table 2. Overall, \(Y\)(North) error was higher than X(East) error. The RMSE\({}_{\text{2D}}\) values ranged between 22.9 m and 26.1 m, mainly due to the presence of large systematic errors (bias). The errors in terms of SD (random errors) were around one meter when they were calculated in one dimension, while they ranged from 1.27 m to 1.52 m in planimetry (2D). These values of SD can be considered the best possible geopositioning accuracy results when applying only a simple shift (zero order transformation, RPC0). Table 3 shows the results of the geometric accuracy for each of the four Deimos-2 images using the RPC0, RPC1 and RPC2 sensor models and the \"all GPs as GCPs\" strategy (i.e., 102 GCPs). The results were very consistent for all the single images, showing that RPC0 was the worst sensor model for Deimos-2 with a mean RMSE\({}_{\text{2D}}\) value of 1.399 m, presenting statistically significant differences (\(p<0.05\)) with respect to both RPC1 and RPC2 sensor models. It is important to note that the mean values of RMSE\({}_{\text{2D}}\) for RPC1 (1.087 m) and RPC2 (1.040 m) sensor models yielded very similar results, not presenting statistically significant differences at a significance level of \(p<0.05\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image Code**} & \multicolumn{2}{c}{**Bias (m)**} & \multicolumn{3}{c}{**SD (m)**} & \multicolumn{3}{c}{**RMSE (m)**} \\ \cline{2-10}  & **X** & **Y** & **X** & **Y** & **2D** & **X** & **Y** & **2D** \\ \hline
1\_1 & 11.12 & -22.10 & 0.97 & 0.82 & 1.27 & 11.16 & 22.12 & 24.77 \\
1\_2 & 11.86 & 23.22 & 1.11 & 1.04 & 1.52 & 11.91 & 23.25 & 26.12 \\
2\_1 & 7.34 & -21.65 & 0.86 & 1.07 & 1.37 & 7.38 & 21.67 & 22.90 \\
2\_2 & 9.20 & 23.05 & 0.91 & 1.14 & 1.46 & 9.24 & 23.08 & 24.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Direct geopositioning accuracy at 102 ICPs (without GCPs) given as bias, standard deviation (SD) and RMSE for the four single Deimos-2 images. The geopositioning accuracy results depending on the number of GCPs (no. GCPs) and sensor model for each of the four Deimos-2 single images are depicted in Table 4. Residuals were measured in the remaining available GPs (i.e., taken as ICPs) in each case. The ANOVA test showed that the applied sensor model presented a significant influence (\(p<0.05\)) on the variance model. The other sources of variation, no. GCPs and the cross-interactions between sensor model and no. GCPs, were not statistically significant. The mean values of the twelve RMSE\({}_{\text{2D}}\) values obtained for each of the three sensor models tested are shown in the first column of Table 4 (values in brackets). The worst statistically significant accuracy results were provided by the RPC0 sensor model, presenting a superscript letter \"a\" different from the superscript letter \"b\" of RPC1 and RPC2. The same letter \"b\" in RPC1 and RPC2 indicates that there are no statistical differences between these two sensor models (\(p<0.05\)). The RMSE\({}_{\text{2D}}\) mean values computed with 6, 12 and 18 GCPs were somewhat worse than the results depicted in Table 3 using 102 GCPs, especially for the RPC1 and RPC2 models. The variable no. GCPs obviously influenced the attained results. In fact, a decreasing trend in the RMSE\({}_{\text{2D}}\) values was observed for the RPC1 and RPC2 models as the number of GCPs increased. However, the differences related to the no. GCPs did not