age, 
1ted 
  
  
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
expected to deteriorate with increasing area size and rotation 
of the satellite during imaging (which introduces 
nonlinearities), while the 3D affine model should perform 
worse with increasing height range and in such cases is more 
sensitive than the 2D affine model in the selection of GCPs. 
3.2 Measurements of the GCPs 
In Geneva, some roundabouts and more straight line 
intersections (nearly orthogonal with at least 10 pixels length) 
were measured semi-automatically in the satellite images and 
the aerial orthoimages (see Fig. 4). Measurement of GCPs by 
least squares template matching (Baltsavias et al., 2001) was 
not convenient or possible due to highly varying image content 
and scale. The height was interpolated from the. DTM used in 
the orthoimage generation. An unexpected complication was 
the fact that the Canton of Geneva is using an own coordinate 
system and not the Swiss one! The transformation from one 
system to the other is not well defined, and based on different 
comparisons of transformed Geneva coordinates and respective 
coordinates in the Swiss system, a systematic bias has been 
observed, indicating that the results listed below could have 
been better. In Thun, the same image measurement approach 
was used, however, roundabouts (which are better targets) 
were very scarce. As expected, well-defined points were 
difficult to find in rural and mountainous areas, especially in 
Thun, where they had to be visible in 5 images simultaneously, 
while shadows and snow made their selection even more 
difficult. The object coordinates in Thun were measured with 
differential GPS. GPS requires work in the field, but the 
accuracy obtained is higher (espec. in height) and more 
homogeneous than using measurements in orthoimages, which 
have varying accuracy with unknown error distribution (due to 
the DSM/DTM). The number of GCPs and their accuracy are 
listed in Table 1. 
   
i So 
Figure 4. Examples of GCP measurement with ellipse fitting 
(left) and line intersection (right). 
3.3 Comparison of different sensor models 
In Geneva, we compared various sensor models, IKONOS vs. 
QB and analysed the influence of the number of GCPs. Due to 
lack of space, only the most important results will be shown 
here. 
Tables 5 and 6 show the results for the transformation from 
object to image space. Three different GCP configurations are 
used with all, 10 and 4 GCPs. Table 5 shows that with all 
GCPs, in IKONOS-East, all 4 sensor models have similar 
performance, with RPC2 being slightly better. In IKONOS- 
West (with forward scanning) results are similar for RPCI and 
RPC2, a bit worse in y with 2D affine and considerably worse 
for 3D affine. The latter model deteriorates more with 
reduction of GCPs and is more sensitive to their selection. For 
the other models, the accuracy reduction from 44 to 4 GCPs is 
very modest, verifying findings from previous investigations 
that the number of GCPs is not so important, as their accuracy 
and secondary their distribution. The results for the 3D affine 
were initially by some factors worse than the ones of Table 5, 
when using geographic coordinates instead of map coordinates 
(oblique Mercator). The dependency of the results on the 
coordinate system has been discussed by Fraser (2004), albeit 
with smaller differences than the ones noted here. 
  
x-RMS | y-RMS Max. | max. 
  
  
  
  
  
  
  
  
  
  
  
Model GCP | CP [m] m] Ax Ay 
[m] [m] 
rpcl 44 - 0.65 0.56 1.40 121 
rpc2 44 - 0.54 0.42 1.53 0.98 
3daff 44 - 0.55 0.41 1.40 | 0.81 
2daff 44 - 0.55 0.47 1.39 1.18 
rpc2 10.:1-34.1.:.0.57 0.52 1.52 1.07 
rpc2 4 40 | 0.60 0.50 1.63 1.13 
rpcl 4 30 | 0.63 0.40 1.35 | 1.40 
rpc2 4 30 | 0.61 0.54 1.63.4 1.13 
3daff 4 30.| 1.25 4.16 | 3.83 | 15.70 
  
  
  
  
  
  
  
  
  
2daff 41/30] 0.66 | 083 |°1,39 | 1.32 
  
Table 5. Comparison of sensor models and number of GCPs 
with IKONOS-East (Geneva). At the bottom, one example for 
IKONOS-West. CP are the check points. 
QB (see Table 6) is much less linear than IKONOS (expected 
partly due to its less stable orbit and pointing, and continuous 
rotation during imaging). Only RPC2 performs with submeter 
accuracy and only with this model can QB achieve similar 
accuracy as IKONOS. A residual plot with RPCI shows a very 
strong x-shear. The 2D and 3D affine transformations are 
totally insufficient for modelling. As with IKONOS, a 
reduction of the GCPs has not any significant influence with 
RPC2. Thus, using simple RPCs (as in most commercial 
systems), or even applying 2 shifts in addition, will not lead to 
very accurate results with QB. It should be noted here that the 
QB image was Basic, i.e. not rectified. It is expected that a 
rectified image will show a more linear behaviour, and the 
respective RPCs will be more stable. 
  
x-RMS | y-RMS | max. max. 
Model | GCP | CP Im] Im] Aximi | Av [ml 
  
  
  
  
rpcl 67 - 2.64 0.43 5:87 0.92 
rpc2 67 - 0.44 0.43 1.06 0.93 
.3daff 67 - 12.96 7.47 28:52 | 22.11 
2daff 67 - 8.26 4.83 19.49 15.53 
  
  
rpc2 10 | 57 | 0.46 0.44 1.12 0.97 
rpc2 4 63 | 0.49 0.57 1.34 1.23 
Table 6. Comparison of sensor models and number of GCPs 
with QB. CP are the check points. 
  
  
  
  
  
  
  
  
  
  
For the Thun dataset, the triplet and stereo images were used 
separately in a bundle adjustment to determine object 
coordinates (processing of all images together was not possible 
due to a program limitation). Several semi-automatically 
measured (with least squares matching) tie points were 
included. The results for the triplet are shown in Table 7. The 
previous conclusions were verified, while the 3D affine model 
was worse compared to Geneva, probably because of the larger 
height range. A new indication compared to the Geneva data 
refers to the height accuracy. This is clearly better with RPC2, 
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