Massively parallel computing using commodity components
Parallel Computing - Parallel computing on clusters of workstations
Journal of Parallel and Distributed Computing
MODTRAN on supercomputers and parallel computers
Parallel Computing
Scalability versus execution time in scalable systems
Journal of Parallel and Distributed Computing
A data and task parallel image processing environment
Parallel Computing - Parallel computing in image and video processing
A software architecture for user transparent parallel image processing
Parallel Computing - Parallel computing in image and video processing
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Parallel and Adaptive Reduction of Hyperspectral Data to Intrinsic Dimensionality
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Parallel Computing on Heterogeneous Networks
Parallel Computing on Heterogeneous Networks
A distributed spectral-screening PCT algorithm
Journal of Parallel and Distributed Computing
Distributed frameworks and parallel algorithms for processing large-scale geographic data
Parallel Computing - Special issue: High performance computing with geographical data
A dynamic earth observation system
Parallel Computing - Special issue: High performance computing with geographical data
High performance air pollution modeling for a power plant environment
Parallel Computing - Special issue: Parallel and distributed scientific and engineering computing
Scheduling Strategies for Master-Slave Tasking on Heterogeneous Processor Platforms
IEEE Transactions on Parallel and Distributed Systems
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Scalability Analysis of Matrix-Matrix Multiplication on Heterogeneous Clusters
ISPDC '04 Proceedings of the Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks
On performance analysis of heterogeneous parallel algorithms
Parallel Computing
Journal of Parallel and Distributed Computing
Guest editorial: Heterogeneous computing
Parallel Computing - Heterogeneous computing
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
High Performance Computing in Remote Sensing
High Performance Computing in Remote Sensing
Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing
Integration, the VLSI Journal
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Imaging spectroscopy, also known as hyperspectral imaging, is a new technique that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. In particular, NASA is continuously gathering high-dimensional image data from the surface of the earth with hyperspectral sensors such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion hyperspectral imager aboard NASA's Earth Observing-1 (EO-1) spacecraft. Despite the massive volume of scientific data commonly involved in hyperspectral imaging applications, very few parallel strategies for hyperspectral analysis are currently available, and most of them have been designed in the context of homogeneous computing platforms. However, heterogeneous networks of workstations represent a very promising cost-effective solution that is expected to play a major role in the design of high-performance computing platforms for many on-going and planned remote sensing missions. Our main goal in this paper is to understand parallel performance of hyperspectral imaging algorithms comprising the standard hyperspectral data processing chain (which includes pre-processing, selection of pure spectral components and linear spectral unmixing) in the context of fully heterogeneous computing platforms. For that purpose, we develop an exhaustive quantitative and comparative analysis of several available and new parallel hyperspectral imaging algorithms by comparing their efficiency on both a fully heterogeneous network of workstations and a massively parallel homogeneous cluster at NASA's Goddard Space Flight Center in Maryland.