## 5 Discussion  
We upgraded the previous automatic GCPs extraction algorithm, which originally performed well for VHR images up to resolutions of around 2 m, with the aim to also work successfully for panchromatic VHR images with resolutions up to 0.3 m. The upgrade concentrated on the road detection method, which left all other parts mostly unchanged. The results demonstrate the ability of the proposed automatic extraction method to obtain GCPs with sub-pixel accuracy. The GCPs residuals are below 1 pixel for all 14 test Pleiades images, while their RMSEs are mainly just above 1 pixel (Table 2). Very similar results can be seen when manual ICPs are employed. Although there are some outliers, the majority of residuals are around 0.5 pixels, which results in final RMSEs that are even lower than those achieved with GCPs. This indicates that small random errors are still present in GCPs, but, as there are no systematic errors generated by the extraction algorithm, the high number of points can still compensate for random errors. The precision of the extracted points can be hardly visually evaluated on the produced GCP chips images since the sub-pixel accuracy level is reached (see Figure 5). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Affine**} & \multicolumn{2}{c}{**Offset**} \\ \hline
**Image** & **Number GCPs** & **GCP RMSE [px]** & **ICP RMSE [px]** & **GCP RMSE [px]** & **ICP RMSE [px]** \\ \hline  & 228 & 1.44 & 0.80 & 1.44 & 0.80 \\  & 200 & 1.47 & 0.80 & 1.47 & 0.79 \\  & 149 & 1.59 & 0.83 & 1.59 & 0.80 \\
4 & 100 & 1.54 & 0.79 & 1.54 & 0.79 \\  & 51 & 1.44 & 0.81 & 1.44 & 0.76 \\  & 29 & 1.51 & 0.84 & 1.57 & 0.76 \\  & 24 & 1.81 & 1.03 & 1.94 & 0.84 \\  & 9 & 1.31 & 0.97 & 1.44 & 0.79 \\ \hline  & 147 & 1.15 & 0.82 & 1.15 & 0.81 \\  & 101 & 1.19 & 0.82 & 1.20 & 0.81 \\
11 & 52 & 1.24 & 0.81 & 1.29 & 0.81 \\  & 28 & 1.10 & 0.86 & 1.13 & 0.85 \\  & 24 & 0.92 & 0.99 & 1.03 & 0.93 \\  & 9 & 1.07 & 1.06 & 1.19 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSEs in pixels at GCPs and ICPs obtained with different numbers of GCPs and both refinement methods. The results are shown for two Pleiades images. Figure 13: Comparison of RMSEs in pixels at ICPs for the affine and offset correction method when all GCPs are used. The proposed method outperforms the previous GCP extraction method, which uses a different road detection algorithm. The previous method has problems in detecting broad roads and roads in densely built areas. It only performs slightly better in Kranj AOI images where the percentage of urban areas is lower when comparing it with the proposed method. This statement is supported by the tests that were performed with the GCPs inside the city perimeter. The results, when using only points inside the urban areas, are unusable in most cases when the points are extracted by the previous method. In those cases, the ICP RMSEs are not correlated to the image size or AOI. Although the number of GCPs in Kranj images is relatively high (and mostly higher than in Ljubljana images with almost twice the area), they are significantly reduced when confining them to the city area. Although the city area is around 21% of the image, only 14% of points are present in this area. For the proposed method the percentage is 31%, which means that relatively more points are present within the boundaries of the city. It can be concluded that the previous method can achieve slightly better results than the proposed method with only GCPs extracted in predominantly non-urban areas where the roads are narrower and can be easily separated from the background that is mostly composed by vegetation. Some images achieve worse results than with the unbiased RPC coefficients if only GCPs of urban areas are used (without GCPs, see Figures 8 and 11). The number of extracted GCPs with the proposed method mostly depends on the quantity of roads on the target images. Good results are obtained with all types of roads and different land covers.