On the Use of Cluster Computing Architectures for Implementation of Hyperspectral Image Analysis Algorithms
- 16 August 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 995-1000
- https://doi.org/10.1109/iscc.2005.114
Abstract
Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the airborne visible infra-red imaging spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wild fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications require a response in real time, few solutions are available to provide fast and efficient analysis of these types of data. This is mainly caused by the dimensionality of hyperspectral images, which limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In the present work, we describe a new parallel methodology which deals with most of the previously addressed problems. The computational performance of the proposed analysis methodology is evaluated using two parallel computer systems, a SGI Origin 2000 shared memory system located at the European Center of Parallelism of Barcelona, and the Thunderhead Beowulf cluster at NASA's Goddard Space Flight Center.Keywords
This publication has 11 references indexed in Scilit:
- A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profilesPattern Recognition, 2004
- A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral DataIEEE Transactions on Geoscience and Remote Sensing, 2004
- A dynamic earth observation systemParallel Computing, 2003
- Distributed frameworks and parallel algorithms for processing large-scale geographic dataParallel Computing, 2003
- Hyperspectral ImagingPublished by Springer Nature ,2003
- Spatial/spectral endmember extraction by multidimensional morphological operationsIEEE Transactions on Geoscience and Remote Sensing, 2002
- An automated parallel image registration technique based on the correlation of wavelet featuresIEEE Transactions on Geoscience and Remote Sensing, 2002
- A software architecture for user transparent parallel image processingParallel Computing, 2002
- D-ISODATA: A Distributed Algorithm for Unsupervised Classification of Remotely Sensed Data on Network of WorkstationsJournal of Parallel and Distributed Computing, 1999
- Massively-parallel Fourier-transform spectral imaging and hyperspectral image processingOptics & Laser Technology, 1993