Journal of VLSI Signal Processing Systems - Special issue: application specific array processors
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
MODTRAN on supercomputers and parallel computers
Parallel Computing
Computer architecture: a quantitative approach
Computer architecture: a quantitative approach
A software architecture for user transparent parallel image processing
Parallel Computing - Parallel computing in image and video processing
Optimizing parallel performance of unstructured volume rendering for the Earth Simulator
Parallel Computing - Parallel graphics and visualisation
Hyperspectral Image Compression on Reconfigurable Platforms
FCCM '02 Proceedings of the 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
A distributed spectral-screening PCT algorithm
Journal of Parallel and Distributed Computing
Beowulf Cluster Computing with Linux
Beowulf Cluster Computing with Linux
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
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
64-bit floating-point FPGA matrix multiplication
Proceedings of the 2005 ACM/SIGDA 13th international symposium on Field-programmable gate arrays
International Journal of High Performance Computing Applications
On-Board Partial Run-Time Reconfiguration for Pico-Satellite Constellations
AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
A robust framework for real-time distributed processing of satellite data
Journal of Parallel and Distributed Computing
Hyperspectral Data Exploitation: Theory and Applications
Hyperspectral Data Exploitation: Theory and Applications
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Clusters versus GPUs for parallel target and anomaly detection in hyperspectral images
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
A new parallel tool for classification of remotely sensed imagery
Computers & Geosciences
Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing
Integration, the VLSI Journal
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Hyperspectral imaging is a new technique in remote sensing that generates images with hundreds of spectral bands, at different wavelength channels, for the same area on the surface of the Earth. Although in recent years several efforts have been directed toward the incorporation of parallel and distributed computing in hyperspectral image analysis, there are no standardized architectures for this purpose in remote sensing missions. To address this issue, this paper develops two highly innovative implementations of a standard hyperspectral data processing chain utilized, among others, in commercial software tools such as Kodak's Research Systems ENVI software package (one of the most popular tools currently available for processing remotely sensed data). It should be noted that the full hyperspectral processing chain has never been implemented in parallel in the past. Analytical and experimental results are presented in the context of a real application, using hyperspectral data collected by NASA's Jet Propulsion Laboratory over the World Trade Center area in New York City, shortly after the terrorist attacks of September 11th 2001. The parallel implementations are tested in two different platforms, including Thunderhead, a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center, and a Xilinx Virtex-II field programmable gate array (FPGA) device. Combined, these platforms deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel processing systems into realistic hyperspectral imaging problems.