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Proceedings Paper

A grid enabled Monte Carlo hyperspectral synthetic image remote sensing model (GRID-MCHSIM) for coastal water quality algorithm
Author(s): Gen-Tao Chiang; Martin Dove; Stuart Ballard; Charles Bostater; Ian Frame
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Paper Abstract

Previous studies indicate that parallel computing for hyperspectral remote sensing image generation is feasible. However, due to the limitation of computing ability within single cluster, one can only generate three bands and a 1000*1000 pixels image in a reasonable time. In this paper, we discuss the capability of using Grid computing where the so-called eScience or cyberinfrastructure is utilized to integrate distributed computing resources to act as a single virtual computer with huge computational abilities and storage spaces. The technique demonstrated in this paper demonstrates the feasibility of a Grid-Enabled Monte Carlo Hyperspectral Synthetic Image Remote Sensing Model (GRID-MCHSIM) for coastal water quality algorithm.

Paper Details

Date Published: 13 October 2006
PDF: 11 pages
Proc. SPIE 6360, Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2006, 636009 (13 October 2006); doi: 10.1117/12.689967
Show Author Affiliations
Gen-Tao Chiang, Univ. of Cambridge (United Kingdom)
Martin Dove, Univ. of Cambridge (United Kingdom)
Stuart Ballard, Univ. of Cambridge (United Kingdom)
Charles Bostater, Florida Institute of Technology (United States)
Ian Frame, Univ. of Cambridge (United Kingdom)


Published in SPIE Proceedings Vol. 6360:
Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2006
Charles R. Bostater; Xavier Neyt; Stelios P. Mertikas; Miguel Vélez-Reyes, Editor(s)

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