Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
The design and analysis of a cache architecture for texture mapping
Proceedings of the 24th annual international symposium on Computer architecture
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
The Cg Tutorial: The Definitive Guide to Programmable Real-Time Graphics
The Cg Tutorial: The Definitive Guide to Programmable Real-Time Graphics
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
IEEE Micro
GPGPU: general purpose computation on graphics hardware
ACM SIGGRAPH 2004 Course Notes
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Journal of Computational Physics
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
Generic visual analysis for multi- and hyperspectral image data
Data Mining and Knowledge Discovery
Parallel unsupervised Synthetic Aperture Radar image change detection on a graphics processing unit
International Journal of High Performance Computing Applications
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Hyperspectral analysis algorithms exhibit inherent parallelism at multiple levels, and map nicely on high performance systems such as massively parallel clusters and networks of computers. Unfortunately, these systems are generally expensive and difficult to adapt to onboard data processing scenarios, in which low-weight and low-power integrated components are desirable to reduce mission pay-load. An exciting new development in this field is the emergence of programmable graphics hardware. Driven by the ever-growing demands of game industry, graphics processing units (GPUs) have evolved from expensive, application-specific units into highly parallel and programmable systems which can satisfy extremely high computational requirements at low cost. In this paper, we investigate GPU-based implementations of a morphological endmember extraction algorithm, which is used as a representative case study of joint spatial/spectral techniques for hyperspectral analysis. The proposed implementations are quantitatively compared and assessed in terms of both endmember extraction accuracy and parallel efficiency. Combined, these parts offer a thoughtful perspective on the potential and emerging challenges of implementing hyperspectral imaging algorithms on commodity graphics hardware.