Large Imagery Data Structuring Using Hierarchical Data Format for Parallel Computing and Visualization

  • Chih Jeng Tan
  • J. A. R. Blais
  • Dean A. Provins
Chapter
Part of the The International Series in Engineering and Computer Science book series (SECS, volume 541)

Abstract

In the general context of Earth System Science, satellite imagery collected over large areas of the Earth needs to be properly structured for extensive data processing and general accessibility. The Hierarchical Data Format (HDF) has been designed for dealing with large datasets with computer platform independence. HDF has also been recently selected by NASA for the projects related to the Earth Observing System and Global Change research applications. Following an introduction to HDF and its different versions, a comparison between HDF and relational databases is made and HDF’s applicability in parallel computing and visualization of large satellite imagery is discussed. Examples of experimental projects are then presented with some suggestions for related multiresolution time series applications in global change and environmental science.

Keywords

Data structures Hierarchical Data Format environmental applications parallel computing and visualization 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adam, S., Blais, J. A. R., and Pietroniro, A. (1998). Understanding the Complex Behavior of a Hydro-Ecological System. In Presentation at Quebec 1998 Geological Association of Canada, Minerological Association of Canada, Association Professionnelle des Géologues et des Géophysiciens du Québec and Canadian Geophysical Union Meeting.Google Scholar
  2. Ahac, A. A., Defoe, R., and van Wijk, M. C. (1992). Considerations in the Design of a System for the Rapid Acquisition of Geographic Information. Photogrametric Engineering and Remote Sensing, 58(1):95–100.Google Scholar
  3. Blais, J. A. R., Gourdeau, D., and Stewart, C. (1997). The Crown of the Continent Ecosystem Data Atlas Project— A Geomatics Perspective. Geomatica, 51(3):235–245.Google Scholar
  4. Blais, J. A. R., He, K., and Larouche, C. (1994). Spatial Rendering and Visualization in Geomatics for Environmental and Related Applications. Geomatica, 48(1):23–31.Google Scholar
  5. Brislawn, C. M. (1995). Fingerprints Go Digital. Notices of the American Mathematical Society, 42(11):1278–1283.Google Scholar
  6. Cheng, A., Folk, M., and Li, R.-H. (1996). HDF Support for Network of Workstations. National Center for Supercomputing Applications.Google Scholar
  7. Cohen, J. S. (1994). Using Artificial Intelligence to Understand EOS Data. The Earth Observer.Google Scholar
  8. Gailly, J.-L. (1993). gzip: The data compression program. Free Software Foundation.Google Scholar
  9. Gallop, J. (1994). State of the Art in Visualization Software. In Hearnshaw, H. M. and Unwin, D. J., editors, Visualization in Geographical Information Systems, chapter 6. John Wiley and Sons Ltd.Google Scholar
  10. Guo, M. (1999). Large Imagery Handling in Relational Databases for Geospatial Information Systems. Master’s thesis, Department of Geomatics Engineering, University of Calgary.Google Scholar
  11. Karimi, H. A. and Blais, J. A. R. (1996). Current and Future Directions in GISs. Computers, Environment and Urban Systems, 20(2):85–97.CrossRefGoogle Scholar
  12. Lousma, J. R. (1993). Rising to the Challenge: The Role of the Information Sciences. Photogrametric Eng’ing and Remote Sensing, 59(6):957–959.Google Scholar
  13. National Center for Supercomputing Applications (1998). HDF User’s Guide. National Center for Supercomputing App., Version 4.1 Release 2 edition.Google Scholar
  14. National Center for Supercomputing Applications (1999). Introduction to HDF5 Release 1.0. National Center for Supercomputing Applications.Google Scholar
  15. Provins, D. A. (1998). Hierarchical Data Form at: A Platform for Research. Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada.Google Scholar
  16. Short, Jr., N. M., Crompt, R. F., Campbell, W. J., Tilton, J. C., LeMoigne, J. L., Fekete, G., Netanyahu, N. S., and Sylvain, G. (1994). AI Challenges within NASA’s Mission to Planet Earth. In AI Technologies for Environmental Applications, Workshop Notes, National Conference on Artificial Intelligence, pages 1–15. National Aeronautics and Space Administration.Google Scholar
  17. Szabo, D. (1998). Near-Regular Triangular Tesselation of the Sphere with OpenGLRendering. Software Notes. Department of Geomatics Engineering, University of Calgary, Alberta, Canada.Google Scholar
  18. Tan, C. J. (1998). High-Performance Visualization of Large Hierarchical Data Format Data on Low-Cost Parallel Computers. Technical report, Department of Geomatics Engineering, University of Calgary, Alberta, Canada.Google Scholar
  19. Zhou, W. (1995). Large Object Support Using an Object-Oriented Approach in Spatial Information Systems. PhD thesis, Department of Geomatics Engineering, University of Calgary. UCGE Report 20084.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Chih Jeng Tan
    • 1
  • J. A. R. Blais
    • 1
  • Dean A. Provins
    • 1
  1. 1.Department of Geomatics EngineeringUniversity of CalgaryCalgaryCanada

Personalised recommendations