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.
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© 2002 Kluwer Academic Publishers
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Tan, C.J., Blais, J.A.R., Provins, D.A. (2002). Large Imagery Data Structuring Using Hierarchical Data Format for Parallel Computing and Visualization. In: Pollard, A., Mewhort, D.J.K., Weaver, D.F. (eds) High Performance Computing Systems and Applications. The International Series in Engineering and Computer Science, vol 541. Springer, Boston, MA. https://doi.org/10.1007/0-306-47015-2_39
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DOI: https://doi.org/10.1007/0-306-47015-2_39
Publisher Name: Springer, Boston, MA
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