Figure 3. Cluster environment: one workstation and multiple 
servers 
Many service operations, image orthorectification and 
orthomosaic creation (including calculation of linecuts) on a 
specialized computer cluster can be done very effectively. We 
have run first tests in 2010 on KazNTU (Kazakh National 
Technical University named after K.I. Satpaev) computing 
cluster, with peak productivity of 10.9 teraflops, NetApp 
storage system running Windows HPS Server OS. Orthomosaic 
creation (scale 1:2000) using precomputed DTM (DEM, refined 
by structured lines) for the area of 1289 sq. km. took 3 hours. 40 
computing nodes were used with the average cpu load of 85%. 
The aerial block had 3558 original images, taken by UltraCam 
XP, with average GSD of 15 cm. The total amount of output 
orthomosaic was 156Gb. 
  
  
Figure 4. Aerial block and cpu load on a computer cluster 
3. VHR AND HR SATELLITE IMAGES 
CLUSTER PROCESSING 
Current VHR and HR satellites (QuickBird, WorldView-1 
WorldView-2, IKONOS, GeoEye-1, Kompsat-2, EROS-B, 
Cartosat-2 ) productivity is more than 2,500,000 sq.km per day. 
The overall productivity of VHR sensors will increase twice in a 
couple of years with launching of the new sattelites. It is now 
impossible to process this amount of data in real time on a 
single DPW. The accuracy of current sensors is sufficient to 
build ortho mosaics with the corresponding accuracy of scale 
1:5000 using only RPC coefficients and without ground control 
points. 
The algorithms of processing such images to produce ortho 
mosaics are fairly simple assuming one has DEM for the needed 
arca. The images are preprocessed one by one, if needed, 
orthorectified one by one using RPC and a given DEM. Cutlines 
and radiometric balancing are computed using neighbouring 
images. At the last step the final ortho mosaic is created and 
saved in the predefined standard sheets. All steps can be run in 
parallel on the computer cluster. 
Special software plus software computer cluster complex were 
developed in Racurs company (Russia). It creates 
photogrammetric project from an arbitary set of satellite images, 
performs bundle adjustment using RPC, computes orthoimages 
using existing DEM, excludes cloudy images or cloud areas, 
builds cutlines, computes tie point for better stitching of 
orthoimages and performs final seamless orthomosaic 
computation with color balancing. The efficiency of this fully 
automatic complex is 1.000.000 sq. km. per day for GeoEye, 
DigitalGlobe or Alos images. Our tests show good efficiency 
and good scalability of the developed hardware-software 
solution. 
  
Figure 5. Computer cluster hardware 
4. ALGORITHMS USED 
Most of processing steps described in the previous section are 
straightforward and described in many papers. We will give 
more details on some algorithms we used to increase the quality 
of the ortho mosaics and to automate the processing. 
The automatic cut lines are build as follows. We use the 
Voronoy diagram to build of preliminary lines: the whole area 
of interest is divided into the set of nonoverlapping image 
patches around images centers. After that the diagram's edges 
are replaced by polylines providing the best junction of the 
images. It's achieved with a help of penalty function that 
includes evaluation of conformity of images, their 
heterogeneity, intensity and gradient lines. For each edge the 
optimal polyline is calculated using dynamic programming 
algorithms. If M(L) is the maximum of the penalty function for 
the polyline L, the algorithm is to find a polyline L, such that 
M(L()= min M(L) among all the polylines L from the starting 
point to the end vertex of the edge. The method can be 
described as finding the best route by “water flood”. The 
following screenshot demonstrates the algorithm. The 
distributed computations are based on parallel considering of 
polylines. 
106 
  
  
Fig 
  
Fig 
clo 
On 
loo 
linc 
be : 
to c 
on.