The SGM method itself uses a smoothness constraint that 
penalizes neighboring pixels with different depth values. The 
matching cost and the smoothness constraint are expressed in 
a global cost function that is minimized by pathwise 
approximation. SGM is not sensitive to the choice of 
parameters, which is very important in practice since hand 
tuning of parameter is avoided. 
The inner processing loop of SGM is very regular and only 
compares and adds integer values: This allows very efficient 
CPU implementations using vector commands as well as 
implementations on graphic cards (Ernst and Hirschmüller, 
2008) and FPGAs (Gehrig et al., 2009; Hirschmüller, 2011). 
SGM produces dense matching results in the resolution of the 
input images (Figure 3). Typically, fine structures as well as 
sharp object boundaries are precisely reconstructed. 
Therefore, the method is very well suited for matching 
airborne as well as satellite images (Gehrke et al., 2010; 
Hirschmiiller and Bucher, 2010). 
4. TESTS AND RESULTS 
The presented method for automatic processing has been 
tested with many different sets of stereo imagery from 
QuickBird II (QB2), WorldView I (WV 1) and WorldView II 
(WV02), kindly provided by DigitalGlobe. From many 
successfully processed scenarios from 16 different sites of the 
world, the results of four exemplary cases were selected to be 
presented in detail. 
The first scenario consists of two WorldView I stereo triples 
of Berlin (Figure 4), an urban area with some small lakes and 
soft elevations. Every triple, consisting of a forward, nadir 
and backward scan, was taken within a few minutes from the 
same orbit. Experience has shown that with this configuration 
the best relative orientation between the images can be 
achieved. Indeed, the relative accuracy over all images was 
very high (Table 2), and the epipolarity error e, below half 
the GSD. This means that the DSM can be generated at the 
original resolution (GSD) of 0.55 m. 
The second scenario is CapeTown (Figure 5), a mainly flat 
urban area with high elevations in between and a coastline to 
the sea. All nine images were captured at different seasons. 
Therefore, the relative accuracy is much lower than in the 
previous scenario. But at least a DSM with a GSDpsm of 
2: e, = 0.83 m was achieved. 
For the mountainous Dunedin scenario four stereo pairs were 
used. Two of them were taken by WorldView I and two by 
WorldView II. A similar accuracy as in as the Cape Town 
scenario was achieved. 
  
Scene Berlin 1 Cape Dunedin | Berlin 2 
Town 
  
1 WV1 2*2 WVI 
* > , * 
Sensors |2*3 WVI 8 WV2 2:2 WV2 2320 QB2 
Area 500 km? 430 km? 580 km? 700 km? 
GS Dax 10:55 m 0.55 m 0.57 m 0.63 m 
€, 0.22 m 0.38 m 0.35 m 2.5m 
€, 4.46 m 1.84 m 10.1 m 4.24 m 
€, 0.24 m 0.43 m 0.39 m 1.86 m 
tpreparation | 1.23 hours | 2.15 hours |0.88 hours | 0.25 hours 
GSDpsm |0.55 m 0.76 m 0.70 m 3.72m 
tsGM 40 hours 41 hours 87 hours 0.17 hours 
  
  
  
  
  
  
  
  
Table 2: Results of the tests. e: The RMS of the spatial 
distances of corresponding lines of sight of independently 
selected homologous points (relative accuracy). e; The RMS 
of the distances of the lines of sights from the GCP's absolute 
positions (absolute accuracy). e,: epipolarity error. 
  
Finally, another scenario from Berlin was chosen, consisting 
of two Quickbird stereo pairs. All images were taken at 
different times and seasons. Despite of that, the automatic tie 
point selection worked very well. The achieved relative 
accuracy was much worse than with imagery of the 
WorldView satellites, also in other scenarios not included in 
this paper. Probably this is due to the generally lower 
geometrical precision of the QuckBird in compare to its 
successor WorldView. 
The achieved absolute accuracy (e,), as shown in Table 2, is 
within the range of the absolute pointing accuracy of the 
corresponding satellites. Both, the absolute and relative 
accuracy have been checked with a set of precisely measured 
and manually selected check points not used for bundle 
adjustment in order to provide an independent reference. 
The tests were performed on a Dell PowerEdge T610 with 
two Intel Xeon X5570 Qudcore CPUs at 2.93GHz. For all 
scenarios the manual preparation could be performed in at 
most a half an hour. The automatic preparation time {preperation 
needed until the SGM processing step requires relatively little 
time when compared to the actual SGM processing step on 
the same computer (see Table 2). The Dunedin scenario took 
the longest to process, due to the larger height range, caused 
by the mountains. Berlin 2 only took very little time as 
GSDpsm Was chosen to be 3.72 m because of the low 
accuracy of the relative orientation. 
The time consumption for SGM processing per square 
kilometer depends mainly on the GSDpsm, the number of 
matches and the height range. In Table 2 the processing times 
for the last two steps (SGM matching and orthophoto 
generation) are given for the different scenarios. 
     
Figure 4: Reconstruction of a part of Berlin, textured by 
panchromatic images. 
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