## 4 Experimental Results
### Results of GCPs Inside City Perimeter  
As mentioned in Section 3.1, the acquired images also contain agricultural land and forest patches aside from urban areas. The share of the densely populated city area is 49% for the Ljubljana images, while the percentage is lower for the Kranj images--around 21%. To only analyse the accuracy of the GCP extraction in city areas, the geometric model is only tested with GCPs that are located inside the city perimeter. Figure 10 shows two examples of GCP distribution with the city perimeter taken into account.  
Figure 9: RMSEs in pixels at ICPs for the method with the previous road mask detection algorithm and the method with the proposed detection algorithm. The results are shown by ascending images’ off-nadir viewing angle. The corresponding days of acquisition are given above the bars. No self-evident correlation can be drawn.  
Table 3 presents the geometric model results when only GCPs located inside the city perimeter are used. As expected, in this case, all of the values are worse when compared to the results where all of the GCPs are used; the most significant indicators--RMSEs at ICPs--are above one pixel, except for one image.  
Figure 11 graphically presents the results. Besides the results with the proposed method, the previous method results are also displayed to emphasise the difference of GCP extraction performance in urban environments. For all images, the proposed method greatly improves the performance of the previous method. The RMSE is at least twice smaller in many cases, while, in few cases, the previous method achieves errors even above five pixels. The numbers above the bars denote the decrease in accuracy when only GCPs that are inside the city perimeter are used. As expected, the values are higher for both methods and all images. The decrease is minimal for the proposed method (never exceeding 1 pixel), while the changes for the previous method are substantial and they are rarely less than 0.5 pixel.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & **Number** & **X res.** & **Y res.** & **RMSE X** & **RMSE Y** & **GCP RMSE** & **Number** & **X res.** & **Y res.** & **RMSE X** & **RMSE Y** & **ICP RMSE** \\  & **GCPs** & **[px]** & **[px]** & **[px]** & **[px]** & **[px]** & **ICPs** & **[px]** & **[px]** & **[px]** & **[px]** \\ \hline
1 & 100 & 0.74 & 0.79 & 0.97 & 1.27 & 1.  
The accuracy results are also correlated to the number of GCPs. Figure 12 shows the final number of GCPs that are used in the geometric model after the gross error removal techniques. The graph presents the numbers for both road extraction methods with and without the reduction of the number of GCPs to the city perimeter area. The proposed method extracts significantly more GCPs than the previous method. The number of extracted GCPs seems to be approximately proportional to the image area, which is not true for the previous method that has obvious problems in detecting GCPs in built areas. The proposed method extracts, on average, more than 150 GCPs for the Ljubljana AOI and more than 100 for the Kranj AOI. The corresponding numbers for the previous method are usually less than 50. Note that all other processing parameters are kept unchanged.  
Figure 11: Comparison of RMSEs in pixels at ICPs for the previous and proposed road extraction method when only GCPs inside the city perimeter are used. The decrease in accuracy (higher RMSE) between the RMSEs that were obtained with all GCPs and only city GCPs is given above the bars.