## 4 Results and Discussion
### Dense Image Matching  
The dense image matching algorithm was applied individually to the two sets of image triplets available as 4-band pansharpened products. Using four cores of a 3.50 GHz machine with 32 GB RAM, the 3D point clouds were generated in 10 and 33 h for Pleiades and WorldView-3 datasets, respectively. The direct output are dense 3D photogrammetric point clouds in the LAS file format with one point for each image pixel. In the overlapping area, the Pleiades-based point cloud consists of 169 million points, whereas the WorldView-3-based equivalent has 476 million points. Hence, it's almost three times denser. Overall, the reconstructed point clouds have a regular distribution on ground plane domain, with densities of 4 points/m\({}^{2}\) and of 12 points/m\({}^{2}\) for Pleiades and WorldView-3, respectively. A few small regions were not reconstructed due to occlusions (e.g., large elevation difference between buildings/trees and surrounding ground). Nevertheless, these holes were filled using interpolation algorithms in the following step. Based on a neighborhood search radius of 1 m and a maximum number of 20 nearest neighbors, we interpolated a regular grid structure using robust moving planes interpolation. For each dataset, a digital surface model was generated using the same parameters. Since we wanted to use the direct output of the 3D reconstruction without losing any information, the point clouds were not edited or filtered before the interpolation step. Usually, interpolation strategies tend to smooth the initial elevation values of the 3D point cloud. In order to minimize this effect, we selected a small size of the grid cell (0.5 m \(\times\) 0.5 m) and a relatively small neighborhood definition for the interpolation. To determine the direct degree of smoothing, a raster containing the maximum elevations in each 0.5 m \(\times\) 0.5 m cell was computed. Using this as a reference, the 2.5D Pleiades interpolated model showed an RMSE value of 0.079 m, while the WorldView-3 DSM had 0.098 m. The latter was slightly higher, but these values (at sub-decimetre level) still showed that the smoothing effect of the interpolation will not significantly influence further analysis. The vertical quality of the photogrammetrically derived DSMs was evaluated against the available elevations of the GCPs and CPs (Table 4). The computed mean values for the CPs--0.07 m (Pleiades) and 0.01 m (WorldView-3) and the standard deviations of 0.30 m (Pleiades) and 0.13 m (WorldView-3)--are comparable with the results obtained by [13], who reported mean values ranging between 0.08 and 0.29 m and standard deviations between 0.22 and 0.53 m.  
For Pleiades, we obtained an RMSE in the \(Z\)-direction of 0.29 m for GCPs and of 0.31 m for CPs. The WorldView-3 DSM showed a higher vertical accuracy, with RMSEs of 0.12 m and 0.13 m for GCPs and CPs, respectively. This is because the vertical accuracy of the DSM is directly influenced by the triplet acquisition geometry, especially by the intersection angle on the ground. As described in [41], the vertical accuracy of the DSMs from VHR satellite imagery is influenced by the acquisition geometry, where a wider angle of convergence (>15\({}^{\circ}\)) enhances the height accuracy. In our case, the Pleiades scenes with a narrow convergence angle (12\({}^{\circ}\)) show a lower vertical performance than the WorldView-3 scenes, with a larger convergence angle on the ground (23\({}^{\circ}\)). The two high resolution DSMs derived from Pleiades and WorldView-3 tri-stereo scenes within the WGS84-UTM33 reference system were used as source data for the following single object investigations.