The Journal of Supercomputing

, Volume 75, Issue 3, pp 1323–1335 | Cite as

Noise estimation for hyperspectral subspace identification on FPGAs

  • Germán LeónEmail author
  • Carlos González
  • Rafael Mayo
  • Daniel Mozos
  • Enrique S. Quintana-Ortí


We present a reliable and efficient FPGA implementation of a procedure for the computation of the noise estimation matrix, a key stage for subspace identification of hyperspectral images. Our hardware realization is based on numerically stable orthogonal transformations, avoids the numerical difficulties of the normal equations method for the solution of linear least squares problems (LLS), and exploits the special relations between coupled LLS problems arising in the hyperspectral image. Our modular implementation decomposes the QR factorization that comprises a significant part of the cost into a sequence of suboperations, which can be efficiently computed on an FPGA.


Hyperspectral images Subspace identification Noise estimation Least squares problems FPGAs High performance Energy consumption 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departamento de Ingeniería y Ciencia de ComputadoresUniversitat Jaume ICastellónSpain
  2. 2.Departamento de Arquitectura de Computadores y AutomáticaUniversidad Complutense de MadridMadridSpain

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