Distributed computing model for processing remotely sensed images based on grid computing

With advances in remote-sensing technology, the large volumes of data cannot be analyzed efficiently and rapidly, especially with arrival of high-resolution images. The development of image-processing technology is an urgent and complex problem for computer and geo-science experts. It involves, not...

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Veröffentlicht in:Information sciences Jg. 177; H. 2; S. 504 - 518
Hauptverfasser: Shen, Zhanfeng, Luo, Jiancheng, Huang, Guangyu, Ming, Dongping, Ma, Weifeng, Sheng, Hao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.01.2007
Schlagworte:
Computing model
Distributed and parallel processing
Grid computing
Middleware
Remotely sensed image processing
Web services
Grid computing
Remotely sensed image processing
Web services
Middleware
Distributed and parallel processing
Computing model
ISSN:0020-0255, 1872-6291
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Beschreibung
Zusammenfassung:With advances in remote-sensing technology, the large volumes of data cannot be analyzed efficiently and rapidly, especially with arrival of high-resolution images. The development of image-processing technology is an urgent and complex problem for computer and geo-science experts. It involves, not only knowledge of remote sensing, but also of computing and networking. Remotely sensed images need to be processed rapidly and effectively in a distributed and parallel processing environment. Grid computing is a new form of distributed computing, providing an advanced computing and sharing model to solve large and computationally intensive problems. According to the basic principle of grid computing, we construct a distributed processing system for processing remotely sensed images. This paper focuses on the implementation of such a distributed computing and processing model based on the theory of grid computing. Firstly, problems in the field of remotely sensed image processing are analyzed. Then, the distributed (and parallel) computing model design, based on grid computing, is applied. Finally, implementation methods with middleware technology are discussed in detail. From a test analysis of our system, TARIES.NET, the whole image-processing system is evaluated, and the results show the feasibility of the model design and the efficiency of the remotely sensed image distributed and parallel processing system.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2006.08.020