Journal of Real-Time Image Processing

, Volume 10, Issue 3, pp 469–483

Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs

  • Sergio Sánchez
  • Rui Ramalho
  • Leonel Sousa
  • Antonio Plaza
Original Research Paper


Spectral unmixing is one of the most popular techniques to analyze remotely sensed hyperspectral images. It generally comprises three stages: (1) reduction of the dimensionality of the original image to a proper subspace; (2) automatic identification of pure spectral signatures (called endmembers); and (3) estimation of the fractional abundance of each endmember in each pixel of the scene. The spectral unmixing process allows sub-pixel analysis of hyperspectral images, but can be computationally expensive due to the high dimensionality of the data. In this paper, we develop the first real-time implementation of a full spectral unmixing chain in commodity graphics processing units (GPUs). These hardware accelerators offer a source of computational power that is very appealing in hyperspectral remote sensing applications, mainly due to their low cost and adaptivity to on-board processing scenarios. The implementation has been developed using the compute device unified architecture (CUDA) and tested on an NVidia™ GTX 580 GPU, achieving real-time unmixing performance in two different case studies: (1) characterization of thermal hot spots in hyperspectral images collected by NASA’s Airborne Visible Infra-red Imaging Spectrometer (AVIRIS) during the terrorist attack to the World Trade Center complex in New York City, and (2) sub-pixel mapping of minerals in AVIRIS hyperspectral data collected over the Cuprite mining district in Nevada.


  1. 1.
    Goetz, A.F.H., Vane, G., Solomon, J.E., Rock, B.N.: Imaging spectrometry for Earth remote sensing. Science 228, 1147–1153 (1985)CrossRefGoogle Scholar
  2. 2.
    Green, R.O., Eastwood, M.L., Sarture, C.M., Chrien, T.G., Aronsson, M., Chippendale, B.J., Faust, J.A., Pavri, B.E., Chovit, C.J., Solis, M., Monsch, K.A., Olah, M.R., Williams, O.: Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 65, 227–248 (1998)CrossRefGoogle Scholar
  3. 3.
    Plaza, A., Benediktsson, J.A., Boardman, J., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, J.A., Marconcini, M., Tilton, J.C., Trianni, G.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, 110–122 (2009)CrossRefGoogle Scholar
  4. 4.
    Plaza, A.: Preface to the special issue on architectures and techniques for real-time processing of remotely sensed images. J Real Time Image Process. 4(3), 191–193 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Plaza, A., Du, Q., Chang, Y.-L., King, R.L.: Foreword to the special issue on high performance computing in Earth observation and remote sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(3), 503–507 (2011)CrossRefGoogle Scholar
  6. 6.
    Lee, C.A., Gasster, S.D., Plaza, A., Chang, C.-I., Huang, B.: Recent developments in high performance computing for remote sensing: a review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(3), 508–527 (2011)CrossRefGoogle Scholar
  7. 7.
    Keshava, N.: Spectral unmixing. IEEE Signal Process. Mag. 19(1), 44–57 (2002)CrossRefGoogle Scholar
  8. 8.
    Keshava, N.: A survey of spectral unmixing algorithms. Linc. Lab. J. 14(1), 55–78 (2003)Google Scholar
  9. 9.
    Plaza, A., Martinez, P., Perez, R., Plaza, J.: A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans. Geosci. Remote Sens. 42, 650–663 (2004)CrossRefGoogle Scholar
  10. 10.
    Bioucas-Dias, J.M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J.: Hyperspectral unmixing overview: geometrical, statistical and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5, 354–379 (2012)CrossRefGoogle Scholar
  11. 11.
    Plaza, A. Chang, C.-I.: High performance computing in remote sensing. Computer & Information Science Series. Chapman & Hall/CRC Press/Taylor & Francis, Boca Raton (2007)Google Scholar
  12. 12.
    Plaza, A., Chang, C.-I.: Special issue on high performance computing for hyperspectral imaging. Int. J. High Perform. Comput. Appl. 22, 363–365 (2008)CrossRefGoogle Scholar
  13. 13.
    Paz, A., Plaza, A.: Clusters versus GPUs for parallel automatic target detection in remotely sensed hyperspectral images. EURASIP J. Adv. Signal Process. 2010, 1–18 (2010, Article ID 915639)Google Scholar
  14. 14.
    Plaza, A., Plaza, J., Vegas, H.: Improving the performance of hyperspectral image and signal processing algorithms using parallel, distributed and specialized hardware-based systems. J. Signal Process. Syst. 50, 293–315 (2010)CrossRefGoogle Scholar
  15. 15.
    Tarabalka, Y., Haavardsholm, T.V., Ksen, I., Skauli, T.: Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing. J. Real Time Image Process. 4, 287–300 (2009)CrossRefGoogle Scholar
  16. 16.
    Setoain, J., Prieto, M., Tenllado, C., Tirado, F.: GPU for parallel on-board hyperspectral image processing. Int. J. High Perform. Comput. Appl. 22, 424–437 (2008)CrossRefGoogle Scholar
  17. 17.
    Setoain, J., Prieto, M., Tenllado, C., Plaza, A., Tirado, F.: Parallel morphological endmember extraction using commodity graphics hardware. IEEE Geosci. Remote Sens. Lett. 43, 441–445 (2007)CrossRefGoogle Scholar
  18. 18.
    Sanchez, S., Paz, A., Martin, G., Plaza, A.: Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units. Concurr. Comput. Pract. Exp. 23, 1538–1557 (2011)CrossRefGoogle Scholar
  19. 19.
    Plaza, A., Plaza, J., Paz, A., Sanchez, S.: Parallel hyperspectral image and signal processing. IEEE Signal Process. Mag. 28, 119126 (2011)CrossRefGoogle Scholar
  20. 20.
    Plaza, A., Du, Q., Chang, Y.-L., King, R.L.: High performance computing for hyperspectral remote sensing. IEEE J. Sel. Top. App. Earth Observ. Remote Sens. 4, 528–544 (2011)CrossRefGoogle Scholar
  21. 21.
    Christophe, E., Michel, J., Inglada, J.: Remote sensing processing: From multicore to GPU. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 643–652 (2011)CrossRefGoogle Scholar
  22. 22.
    Yang, H., Du, Q., Chen, G.: Unsupervised hyperspectral band selection using graphics processing units. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 660–668 (2011)CrossRefGoogle Scholar
  23. 23.
    Goodman, J.A., Kaeli, D., Schaa, D.: Accelerating an imaging spectroscopy algorithm for submerged marine environments using graphics processing units. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 669–676 (2011)CrossRefGoogle Scholar
  24. 24.
    Wei, S.-C., Huang, B.: GPU acceleration of predictive partitioned vector quantization for ultraspectral sounder data compression. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 677682 (2011)CrossRefGoogle Scholar
  25. 25.
    Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing, 2nd edn. Academic Press, New York (1997)Google Scholar
  26. 26.
    Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis: An Introduction. Springer, Berlin (2006)Google Scholar
  27. 27.
    Winter, M.E.: N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In: Proceedings of SPIE, vol. 3753, 266–270 (1999)Google Scholar
  28. 28.
    Plaza, A., Valencia, D., Plaza, J., Martinez, P.: Commodity cluster-based parallel processing of hyperspectral imagery. J. Parallel Distrib. Comput. 66, 345–358 (2006)CrossRefMATHGoogle Scholar
  29. 29.
    Chang, C.-I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Kluwer/Plenum Publishers, New York (2003)CrossRefGoogle Scholar
  30. 30.
    Clint, M., Jenning, A.: The evaluation of eigenvalues and eigenvectors of real symmetric matrices by simultaneous iteration, Comput. J. 13, 76–80 (1970)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn, pp. 406–408. Johns Hopkins University Press, Baltimore (1996)Google Scholar
  32. 32.
    Parlett, B.N.: The Symmetric Eigenvalue Problem. Society for Industrial and Applied Mathematics (1998)Google Scholar
  33. 33.
    Parlett, B.N., Dhillon, I.S.: Relatively robust representations of symmetric tridiagonals. Linear Algebra Appl. 309(1–3), 121–151 (2000)MathSciNetCrossRefMATHGoogle Scholar
  34. 34.
    Sleijpen, G.L., Van der Vorst, H.A.: A Jacobi–Davidson iteration method for linear eigenvalue problems. SIAM Rev. 42(2), 267–293 (2000)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Saad, Y.: Numerical Methods for Large Eigenvalue Problems, revised edition. Society for Industrial and Applied Mathematics (2011)Google Scholar
  36. 36.
    Craig, M.D.: Minimum-volume transforms for remotely sensed data. IEEE Trans. Geosci. Remote Sens. 32, 542552 (1994)CrossRefGoogle Scholar
  37. 37.
    Miao, L., Qi, H.: Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Trans. Geosci. Remote Sens. 45(3), 765777 (2007)CrossRefGoogle Scholar
  38. 38.
    Li, J., Bioucas-Dias, J.: Minimum volume simplex analysis: a fast algorithm to unmix hyperspectral data, In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, vol. 3, pp. 250–253 (2008)Google Scholar
  39. 39.
    Chan, T.-H., Chi, C.-Y., Huang, Y.-M., Ma, W.-K.: A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. IEEE Trans. Signal Process. 57, 44184432 (2009)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Sergio Sánchez
    • 1
  • Rui Ramalho
    • 2
  • Leonel Sousa
    • 2
  • Antonio Plaza
    • 1
  1. 1.Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politecnica de CáceresUniversity of ExtremaduraCáceresSpain
  2. 2.INESC-ID, ISTTechnical University of LisbonLisbonPortugal

Personalised recommendations