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Remote Sensing Information Processing Grid Node with Loose-Coupling Parallel Structure

  • Ying Luo
  • Yong Xue
  • Yincui Hu
  • Chaolin Wu
  • Guoyin Cai
  • Lei Zheng
  • Jianping Guo
  • Wei Wan
  • Shaobo Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)

Abstract

To use traditional algorithms and software packages on Grid system, traditional algorithms and software packages, in general, have to be modified. In this paper we focus on standards and methodologies for Grid platform within the context of the Remote Sensing Data Processing Grid Node (RSDPGN) that implements a loose-coupling parallel structure for orchestrating traditional remote sensing algorithms and software packages on the Condor platform. We have implemented 17 remote sensing applications in one system using Web service and workflow technology without any change to traditional codes. Some core algorithm codes are come from a remote sensing software package which we has neither resource codes nor APIs. Others come from the program codes accumulated by our group. The design and prototype implementation of RSDPGN are presented. The advantage and shortage of loose-coupling structure is analysed. Through a case study of land surface temperature calcu-lation from MODIS data, we demonstrate the way to modify software packages in details. Moreover we discuss the problems and solutions based on our experience such as system architecture, the kinds of functional modules, fast data transfer, and state monitoring.

Keywords

Remote Sensing Land Surface Temperature Aerosol Optical Depth Traditional Algorithm Super Computer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Basney, J., Livny, M., Tannenbaum, T.: High Throughput Computing with Condor. HPCU news 1(2) (1997)Google Scholar
  2. 2.
    Xue, Y., Cai, G.Y., Guan, Y.N., Cracknell, A.P., Tang, J.K.: Iterative Self-Consistent Approach For Earth Surface Temperature Determination. International Journal of Remote Sensing 26(1), 185–192 (2005)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ying Luo
    • 1
    • 3
  • Yong Xue
    • 1
    • 2
  • Yincui Hu
    • 1
  • Chaolin Wu
    • 1
    • 3
  • Guoyin Cai
    • 1
  • Lei Zheng
    • 1
    • 3
  • Jianping Guo
    • 1
    • 3
  • Wei Wan
    • 1
    • 3
  • Shaobo Zhong
    • 1
  1. 1.State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Institute of Remote Sensing ApplicationsChinese Academy of SciencesBeijingChina
  2. 2.Department of ComputingLondon Metropolitan UniversityLondonUK
  3. 3.Graduate School of the Chinese Academy of SciencesBeijingChina

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