Title :
Radar image processing with clusters of computers
Author :
Goller, Alois ; Leberl, Franz
Author_Institution :
Inst. for Comput. Graphics & Vision, Tech. Univ. Graz, Austria
Abstract :
Some radar image processing algorithms such as shape-from-shading are particularly compute-intensive and time consuming. If, in addition, a data set to be processed is large, then it may make sense to perform the processing of images on multiple workstations or parallel processing systems. We have implemented shape-from-shading, stereo matching, resampling, gridding and visualization of terrain models in such a manner that they execute either on parallel machines or on clusters of workstations. We were motivated by the large image data set from NASA´s Magellan mission to planet Venus, but received additional inspiration from the European Union´s Center for Earth Observation program (CEO) and Austria´s MISSION initiative for distributed processing of remote sensing images on remote workstations, using publicly accessible algorithms. We have developed a multi-processor approach that we denote as CDIP for Concurrent and Distributed Image Processing. The speedup for image processing tasks increases nearly linearly with the number of processors, be they on a parallel machine or arranged in a cluster of distributed workstations. Our approach adds benefits for users of complex image processing algorithms: the efforts for code porting and code maintenance are reduced and the necessity for specialized parallel processing hardware is eliminated
Keywords :
astronomy computing; computer vision; geophysical signal processing; multiprocessing systems; parallel algorithms; parallel architectures; radar imaging; remote sensing; space research; stereo image processing; terrain mapping; Austria; CDIP; Concurrent and Distributed Image Processing; Earth Observation program; European Union; Magellan mission; NASA; Radar image processing; clusters of computers; code maintenance; code porting; distributed processing; distributed workstations; gridding; image data set; multiple workstations; parallel machines; parallel processing; planet Venus; remote sensing images; remote workstations; resampling; shape-from-shading; stereo matching; terrain models; visualization; Clustering algorithms; Data visualization; Earth; Image processing; Parallel machines; Parallel processing; Planets; Radar imaging; Venus; Workstations;
Conference_Titel :
Aerospace Conference Proceedings, 2000 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
0-7803-5846-5
DOI :
10.1109/AERO.2000.879856