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Commodity cluster-based parallel processing of hyperspectral imagery

Authors:

David Valencia,

Pablo MartinezAuthors Info & Claims

Journal of Parallel and Distributed Computing, Volume 66, Issue 3

Pages 345 - 358

https://doi.org/10.1016/j.jpdc.2005.10.001

Published: 01 March 2006 Publication History

Abstract

The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery.

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Cited By

Hossam MEbied HAbdel-Aziz MTolba M(2018)Accelerated hyperspectral image recursive hierarchical segmentation using GPUs, multicore CPUs, and hybrid CPU/GPU clusterJournal of Real-Time Image Processing10.1007/s11554-014-0464-414:2(413-432)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11554-014-0464-4
Gao LZhuang LWu YSun XZhang B(2016)A quantitative and comparative analysis of different preprocessing implementations of DPSOSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1507-220:12(4669-4683)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s00500-014-1507-2
Goel NSehgal P(2015)Parallel weighted semantic fusion for cross-media retrievalInternational Journal of Computational Intelligence Studies10.1504/IJCISTUDIES.2015.0698324:1(50-71)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1504/IJCISTUDIES.2015.069832
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Index Terms

Commodity cluster-based parallel processing of hyperspectral imagery

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Published In

cover image Journal of Parallel and Distributed Computing

Journal of Parallel and Distributed Computing Volume 66, Issue 3

March 2006

165 pages

ISSN:0743-7315

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2005.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 March 2006

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Cited By

Hossam MEbied HAbdel-Aziz MTolba M(2018)Accelerated hyperspectral image recursive hierarchical segmentation using GPUs, multicore CPUs, and hybrid CPU/GPU clusterJournal of Real-Time Image Processing10.1007/s11554-014-0464-414:2(413-432)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11554-014-0464-4
Gao LZhuang LWu YSun XZhang B(2016)A quantitative and comparative analysis of different preprocessing implementations of DPSOSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1507-220:12(4669-4683)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s00500-014-1507-2
Goel NSehgal P(2015)Parallel weighted semantic fusion for cross-media retrievalInternational Journal of Computational Intelligence Studies10.1504/IJCISTUDIES.2015.0698324:1(50-71)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1504/IJCISTUDIES.2015.069832
Sánchez SRamalho RSousa LPlaza A(2015)Real-time implementation of remotely sensed hyperspectral image unmixing on GPUsJournal of Real-Time Image Processing10.1007/s11554-012-0269-210:3(469-483)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s11554-012-0269-2
van Werkhoven BMaassen JBal HSeinstra F(2014)Optimizing convolution operations on GPUs using adaptive tilingFuture Generation Computer Systems10.5555/2747903.274818230:C(14-26)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.5555/2747903.2748182
van Kessel TDrost NMaassen JBal HSeinstra F(2012)User Transparent Data and Task Parallel Multimedia Computing with Pyxis-DTProceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)10.1109/CCGrid.2012.99(17-24)Online publication date: 13-May-2012
https://dl.acm.org/doi/10.1109/CCGrid.2012.99
Bueno GGonzález RDéniz OGarcía-Rojo MGonzález-García JFernández-Carrobles MVállez NSalido J(2012)A parallel solution for high resolution histological image analysisComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2012.03.007108:1(388-401)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1016/j.cmpb.2012.03.007
van Kessel TDrost NSeinstra F(2010)User transparent task parallel multimedia content analysisProceedings of the 16th international Euro-Par conference on Parallel processing: Part II10.5555/1885276.1885282(38-50)Online publication date: 31-Aug-2010
https://dl.acm.org/doi/10.5555/1885276.1885282
Paz APlaza A(2010)Clusters versus GPUs for parallel target and anomaly detection in hyperspectral imagesEURASIP Journal on Advances in Signal Processing10.1155/2010/9156392010(1-18)Online publication date: 1-Feb-2010
https://dl.acm.org/doi/10.1155/2010/915639
Zhang JWang SWilkins-Diehr NShi XVatsavai RZhang J(2010)Towards personal high-performance geospatial computing (HPC-G)Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems10.1145/1869692.1869694(3-10)Online publication date: 2-Nov-2010
https://dl.acm.org/doi/10.1145/1869692.1869694
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