Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
Hyperspectral Data Exploitation: Theory and Applications
Hyperspectral Data Exploitation: Theory and Applications
High Performance Computing in Remote Sensing
High Performance Computing in Remote Sensing
International Journal of High Performance Computing Applications
Clusters Versus FPGA for Parallel Processing of Hyperspectral Imagery
International Journal of High Performance Computing Applications
GPU for Parallel On-Board Hyperspectral Image Processing
International Journal of High Performance Computing Applications
EURASIP Journal on Advances in Signal Processing
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
Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units
Concurrency and Computation: Practice & Experience
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Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require advanced on-board processing algorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping of oil spills and chemical contamination, etc. One of the most widely used techniques for analyzing hyperspectral images is spectral unmixing, which allows for sub-pixel data characterization. This is particularly important since the available spatial resolution in hyperspectral images is typically of several meters, and therefore it is reasonable to assume that several spectrally pure substances (called endmembers in hyperspectral imaging terminology) can be found within each imaged pixel. In this paper we explore the role of hardware accelerators in hyperspectral remote sensing missions and further inter-compare two types of solutions: field programmable gate arrays (FPGAs) and graphics processing units (GPUs). A full spectral unmixing chain is implemented and tested in this work, using both types of accelerators, in the context of a real hyperspectral mapping application using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). The paper provides a thoughtful perspective on the potential and emerging challenges of applying these types of accelerators in hyperspectral remote sensing missions, indicating that the reconfigurability of FPGA systems (on the one hand) and the low cost of GPU systems (on the other) open many innovative perspectives toward fast on-board and on-the-ground processing of remotely sensed hyperspectral images.