## 5 Discussion
The proposed method extracts more than 90 points per image in average (while the previous method substantially less), and the density of GCPs that are inside the city perimeter is higher than outside. The points that were obtained by the proposed method are evenly distributed and achieve a higher accuracy when using only city points. In this case, the RMSEs at manually measured ICPs are slightly higher, which can also be attributed to the distribution of these points. As the GCPs are confined to the built area, the errors at ICPs outside the city area are slightly bigger. For this reason, it can be concluded that the extraction of GCPs is feasible and also quite accurate in densely built areas. Another important aspect of the proposed extraction algorithm is its robustness towards different imaging conditions. It is capable of efficiently detecting GCPs on VHR panchromatic images of different dates and different off-nadir angles. From the results, is impossible to tell which image has high off-nadir angles, longest shadows or from which side of the imaged area the images were taken. As the algorithm tries to detect GCPs on all computed crossroads, it is easier to obtain points in built areas where the roads are abundant, although the road detection is often not optimal (mainly because of shadows and occlusions). This algorithm capability makes it even more suitable for the orthorectification of images that mostly cover urban areas. After the geometric corrections, the RMSEs at the GCPs and ICPs are generally in the same range, independent of the number of GCPs that are used in the model refinement. Due to the random errors, higher RMSEs were expected when sets with higher number of GCPs are employed. As this is not the case it can be concluded that the random errors are very small and they are additionally minimized during the refinement. The set size has a bigger impact on the refinement methods. The offset correction performs slightly better when less than 30 GCPs are used for Pleiades imagery. Therefore, the marginally worse results when using small sets can also be attributed to the refinement method. Nevertheless, the difference is small and it is not the main reason for high RMSEs that were obtained with the previous method when using only city GCPs. The refinement method is sensor specific and it should be taken into account in geometric correction algorithms. The proposed method performs well in either rural or urban areas. Rural areas usually exhibit a network of narrower roads that are less occluded by buildings. In such scenario, the extraction of GCPs is straightforward and it results in many points (with good atmospheric conditions) even if the previous method is used. Problems may occur when non-urban areas only feature wide roads (e.g., highways) or big intersections. In such case, the proposed method has a higher chance of extracting accurate GCPs. On the other hand, the road network is always dense in urban areas, which enables the possibility of a high number of GCP candidates. The drawback of urban areas is mostly the occlusions that are mainly created by high buildings, trees, and moving objects (e.g., cars, public transport), and other impervious areas that are similar to roads and hinder the road detection. The urban roads are generally wider than in rural areas, which contributes to a higher accuracy and number of GCPs obtained by the proposed method. It would be reasonable to differentiate the algorithms according to the type of land cover, using the proposed algorithms only on the urban areas, and the previous one on the rest of the image, as the previous method has smaller computational cost. However, the performance of the road detection and GCP extraction is only marginally dependant on the type of land cover and mostly depends on the type of roads present in the image. Additionally, it would need to classify the raw image to urban-non-urban area before the road detection, which would need supplementary procedures and more processing time.