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Dimension Reduction of Hyperspectral Data on Beowulf ClustersTraditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operation. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold a great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, which is used widely in remote sensing, is the Principal Components Analysis (PCA). In light of the growing number of spectral channels of modern instruments, the paper reports on the development of a parallel PCA and its implementation on two Beowulf cluster configurations, on with fast Ethernet switch and the other is with a Myrinet interconnection.
Document ID
20040008401
Acquisition Source
Goddard Space Flight Center
Document Type
Contractor or Grantee Report
Authors
El-Ghazawi, Tarek
(George Mason Univ. Fairfax, VA, United States)
Date Acquired
August 21, 2013
Publication Date
January 1, 2000
Subject Category
Earth Resources And Remote Sensing
Funding Number(s)
CONTRACT_GRANT: USRA-5555-28-71
CONTRACT_GRANT: NAS5-32337
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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