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Parallel real-time virtual dimensionality estimation for hyperspectral images

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Abstract

One of the most important tasks in hyperspectral imaging is the estimation of the number of endmembers in a scene, where the endmembers are the most spectrally pure components. The high dimensionality of hyperspectral data makes this calculation computationally expensive. In this paper, we present several new real-time implementations of the well-known Harsanyi–Farrand–Chang method for virtual dimensionality estimation. The proposed solutions exploit multi-core processors and graphic processing units for achieving real-time performance of this algorithm, together with better performance than other works in the literature. Our experimental results are obtained using both synthetic and real images. The obtained processing times show that the proposed implementations outperform other hardware-based solutions.

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Acknowledgements

The authors gratefully thank NVIDIA Corporation for the donation of the GPU Tesla K40 used for this research.

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Authors and Affiliations

  1. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy

    Emanuele Torti & Alessandro Fontanella

  2. Department of Technology of Computers and Communications, Escuela Politecnica de Caceres, University of Extremadura, Caceres, Spain

    Antonio Plaza

Authors
  1. Emanuele Torti
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  2. Alessandro Fontanella
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  3. Antonio Plaza
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Correspondence to Emanuele Torti.

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Torti, E., Fontanella, A. & Plaza, A. Parallel real-time virtual dimensionality estimation for hyperspectral images. J Real-Time Image Proc 14, 753–761 (2018). https://doi.org/10.1007/s11554-017-0703-6

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  • DOI: https://doi.org/10.1007/s11554-017-0703-6

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