International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
x-RMS | y-RMS | max. max. 
Model | GCP | CP m] [m] Ax [nl lA [ra] 
rpcl 67 - 2.64 0.43 5.57 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 13.33 
rpc2 10 1 57 | 0.46 0.44 1.12 0.97 
rpe2 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. 
As a next step, we checked the role of the area covered by the 
GCPs, using always 5 GCPs (Table 8). RPCI gave more or less 
similar results in planimetry, verifying previous investigations 
with the 2D affine model. The height however, is more 
sensitive to the position of the area covered by the GCPs, 
deteriorating in accuracy when GCPs were only in flat areas. 
Surprisingly, RPC2 gives clearly worse results than RPCI, 
especially when GCPs cover only 1/3 of the image area. This 
has been also verified with the Geneva images. À possible 
explanation is that after the RPCs are used, the scales and 
shears of the affine transformation model very small residual 
model errors. If in addition the GCP measurements are noisy 
(see e.g. the particularly high RMS at the mountainous south- 
west where GCP definition was poor), and the area covered is 
small, then these parameters may easily take wrong values. 
Grodecki and Dial (2003) mention the need to use only a linear 
factor in flight direction if the strip is long (about > 50 km). In 
future investigations, we will analyse to what extent the 4 scale 
and shear parameters are significant and determinable. These 
preliminary results indicate that RPC2 should be used with a 
GCP distribution covering most of the image area. 
4 ORTHOIMAGE AND DSM GENERATION 
The focus in the following text will be on the DSM generation 
in Thun. The results of the orthoimage generation in Geneva are 
analysed in Heller and Gut (2004). The accuracy of the 
orthoimages generated with the laser DTM and RPC2 with 10 
GCPs gave an exceptional accuracy of 0.5 m - 0.80 m for both 
IKONOS and QB, with very typical sensor elevation values. 
These orthoimages are thus more accurate than the national 
Swissimage orthoimages, however interpretation of objects is 
more difficult. 
4.1 DSM Generation Method 
For DSM generation, a hybrid image matching algorithm was 
used (for details see Zhang and Gruen, 2003, 2004). Our 
method considers the characteristics of the linear array image 
data and its imaging geometry. The method can accommodate 
images from very high-resolution (3-7 cm) airborne Three-Line- 
Scanner images to HRS images like IKONOS, QB and SPOT-5. 
It can be used to produce dense, precise and reliable results for 
DSM/DTM generation. The final DSMs are generated by 
combining the matching results of feature points, grid points 
and edges. Matching is performed using cross-correlation and 
image pyramids. A TIN-based DSM is constructed from the 
matched features (whereby edges are used as breaklines) at each 
level of the pyramid, which in turn is used in the subsequent 
pyramid level for approximations and adaptive computation of 
the matching parameters. The modified MPGC (Multiphoto 
Geometrically Constrained Matching) algorithm (Gruen, 1985; 
Baltsavias, 1991) is employed to achieve sub-pixel accuracy for 
all points matched (if possible in more than two images) and 
identify some inaccurate and possibly false matches. Finally, a 
raster DSM can be interpolated from the original matching 
results. 
  
  
Table 8. Different distribution of GCPs in the IKONOS triplet Thun. CP are the check points. In the upper table part the GCPs cover 
1/3 of the image in south-west, south-east, north-east and north-west, respectively (the most mountainous part is south-west, and then 
Table 7. Comparison of sensor models and number of GCPs in the IKONOS triplet (Thun). CP are the check points. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Sensor GCP | CP x-RMS | y-RMS | zRMS | max. | max. Ay | max. Az 
Model [m] [m] [m] Ax [m] [m] [m] 
rpcl 24 - 0.44 0.46 1.06 -1.11 -0.89 2.08 
rpc2 24 - 0.39 0.42 0.68 -0.95 -0.84 -1.40 
3daff 24 - 2:37 1.07 0.86 -4.87 2.05 1.57 
rpc2 20 4 0.40 0.42 0.68 -1.01 -0.93 -1.41 
rpe2 12 12| 041 0.46 0.72 0.90 -0.92 -1.44 
rpc2 5 19] ..0.51 0.43 0.90 -1.37 -0.78 -1.40 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Sensor GCP | cp x-RMS | y-RMS | y-RMS | max. | max. max. 
Model [m] [m] [m] Ax [m] | Ay [m] | Ay Im] 
rpcl 5 19 | 0.45 0.46 1.10 -1.07 -0.99 2.30 
rpc2 S 19 { “0.67 1.70 3.45 1.18 -3.04 6.24 
rpcl 5 19 1 0.50 0.47 1.63 -1.33 0.89 2.93 
rpc2 5 19 1 0.22 0.97 1.75 -1.51 2.02 3.17 
rpcl 5 19 | 0.45 0.46 1:25 -1.05 -0.96 2.74 
rpc2 3 19 1.0.53 0.59 1.50 -1.03 -1.52 3.15 
rpcl 5 19 | 0.49 0.46 1.65 1.06 -1.05 3.35 
rpc2 5 19 | 0.47 0.86 0.92 -0.95 1.95 1.94 
rpcl 5 191 0.45 0.46 1.10 -1.06 -1.16 4.11 
rpc2 5 19 | 0.41 0.70 1.05 -1.18 -1.19 -2.33 
  
  
  
  
  
  
  
  
  
  
  
north-east). In the bottom table part, GCPs cover 2/3 of the image. 
526 
  
  
2) 
3) 
4)