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Using grid computing to enable hyperspectral imaging analysis

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Abstract
Hyperspectral imaging analysis demands large input data sets and in turn re- quires signi¯cant CPU time and memory capacity. Grid Computing has the potential of improving the performance of these types of data and computational intensive applications. In this thesis we describe the design and implementation of Grid-HSI, a Service Oriented Architecture-based Grid application to enable hyperspectral imaging analysis. Grid-HSI provides users with a transparent interface to access computational resources and perform remotely hyperspectral imaging analysis through a set of Grid services. The base of the system is composed by a Portal Grid Interface, a Data Broker and a set of specialized Grid services. The system is based on the Open Grid Service Architecture (OGSA), and imple- mented on the top of Globus Toolkit 3 (GT3). We have conducted experiments that show the suitability of Grid-HSI to perform e±ciently hyperspectral analysis.
Las imágenes hiperespectrales demandan gran tiempo de procesamiento al igual que gran requerimiento de capacidad de memoria. La computación en malla tiene el potencial de mejorar el rendimiento para el análisis de imágenes hyperespactrales y de aplicaciones de computacion intensiva. En esta tesis se describe el dise~no y la implementacion de Grid-HSI, una aplicación en malla basada en una arquitectura orientada a servicio para habilitar el análisis de imágenes hyperespactrales. Grid-HSI provee a los usuarios una inter- fase transparente para acceder a recursos computacionales y ejecutar remotamente análisis de imágenes hyperespectrales a través de un conjunto de servicios grid. El sistema esta basado en Open Grid Services Architecture (OGSA), e implementado con Globos Toolkit 3.0.
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Date
2004
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Keywords
Grid computing, hyperspectral imaging analysis
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