International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August-01 September 2012, Melbourne, Australia 
109 
HIGH PERFORMANCE PHOTOGRAMMETRIC 
PROCESSING ON COMPUTER CLUSTERS 
V. N. Adrov, M. A. Drakin, A. Yu. Sechin 
JCSC Racurs, Moscow, Russia - adrov@racurs.ru, mike@racurs.ru, sechin@racurs.ru 
Commission IV/3: Mapping from High Resolution Data 
KEY WORDS: Satellites, DEM, Digital, Model, Mosaic, Networks, Orthoimage, Orthorectification, Photogrammetry 
ABSTRACT: 
Most cpu consuming tasks in photogrammctric processing can be done in parallel. The algorithms take independent bits as input and 
produce independent bits as output. The independence of bits comes from the nature of such algorithms since images, stereopairs or 
small image blocks parts can be processed independently. Many photogrammctric algorithms are fully automatic and do not require 
human interference. Photogrammctric workstations can perform tie points measurements, DTM calculations, orthophoto 
construction, mosaicing and many other service operations in parallel using distributed calculations. Distributed calculations save 
time reducing several days calculations to several hours calculations. Modern trends in computer technology show the increase of 
cpu cores in workstations, speed increase in local networks, and as a result dropping the price of the supercomputers or computer 
clusters that can contain hundreds or even thousands of computing nodes. Common distributed processing in DPW is usually 
targeted for interactive work with a limited number of cpu cores and is not optimized for centralized administration. The bottleneck 
of common distributed computing in photogrammetry can be in the limited lan throughput and storage performance, since the 
processing of huge amounts of large raster images is needed. 
1. INTRODUCTION 
Many operations in processing remote sensing data can be split 
into numerous tasks that can be easily done in parallel. 
Figure 1. Building orthomosaic from a set of images 
The original images are similar in nature and can be processed 
independently to build ortho mosaics. This is due to the fact that 
cutlines calculation, orthorectification, image format 
conversion, pansharpening, many service opertations performed 
on one image or on few images and are independent on other 
images. Parallel processing of data can reduce the computer 
time needed to get the final result considerably. The current 
trends in computer hardware are the increasing number of cores 
(identical computer units) on the off-the-shelf CPUs, larger and 
faster computer networks and more and more affordable 
supercomputers as clusters of thousands of computer nodes. 
Conventional digital photogrammetric workstation (DPWs) use 
distributed processing that is based on interactive work and uses 
the ideology - many workstations and one server. 
Figure 2. Conventional approach: many workstations and one 
server 
This approach is not efficient when modem computer clusters 
are used. 
2. DISTRIBUTED PROCESSING ON 
COMUTER CUUSTERS 
Special adjustments of software algorithms are needed for 
computer clusters. Such algorithms predict data workflow and 
split the computing tasks into several independent processes. 
The approach is different from the conventional one: the main 
ideology is: one workstation and multiple servers