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Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs

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Abstract

Spectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It amounts at identifying a set of spectrally pure components (called endmembers) and their associated per-pixel coverage fractions (called abundances). A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. Several automatic techniques exist for this purpose, including the virtual dimensionality (VD) concept or the hyperspectral signal identification by minimum error (HySime). Due to the complexity and high dimensionality of hyperspectral scenes, these techniques are computationally expensive. In this paper, we develop new fast implementations of VD and HySime using commodity graphics processing units. The proposed parallel implementations are validated in terms of accuracy and computational performance, showing significant speedups with regards to optimized serial implementations. The newly developed implementations are integrated in a fully operational unmixing chain which exhibits real-time performance with regards to the time that the hyperspectral instrument takes to collect the image data.

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Notes

  1. http://www.nvidia.com/object/cuda_home_new.html.

  2. http://developer.nvidia.com/cuBLAS.

  3. http://www.netlib.org/lapack/.

  4. http://aviris.jpl.nasa.gov/data/free_data.html.

  5. http://speclab.cr.usgs.gov/spectral-lib.html.

  6. http://www.nvidia.com/object/product-geforce-gtx-580-us.html.

References

  1. Goetz, A.F.H.,Vane, G., Solomon, J.E., Rock, B.N.: Imaging spectrometry for Earth remote sensing. Science. 228, 1147–1153 (1985)

    Article  Google Scholar 

  2. Green, R.O.: Imaging spectroscopy and the airborne visible infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 65, 227–248 (1998)

    Article  Google Scholar 

  3. Plaza, A., Benediktsson, J.A., Boardman, J., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, J., Marconcini, M., Tilton, J.C., Trianni G.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, 110–122 (2009)

    Article  Google Scholar 

  4. Plaza, A.: Special issue on architectures and techniques for real-time processing of remotely sensed images J. Real Time Image Process. 4(3), 191–193 (2009)

    Article  MathSciNet  Google Scholar 

  5. Plaza, A., Plaza, J., Paz, A., Sanchez, S.: Parallel hyperspectral image and signal processing . IEEE Signal. Process. Mag. 28(3), 119–126 (2011)

    Article  Google Scholar 

  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. Selected Topics Appl. Earth Observ. Remote Sens. 4(3), 508–527 (2011)

    Article  Google Scholar 

  7. Bioucas-Dias, J.M., Plaza, A.: Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches. In: Proceedings of SPIE, Image and Signal Processing for Remote Sensing XVI 7830:1–15, Toulouse, France (2010)

  8. Plaza, A., Du, Q., Bioucas-Dias, J.M., Jia, X., Kruse, F.: Foreword to the special issue on spectral unmixing of remotely sensed data. IEEE Trans. Geosci. Remote. Sens. 49(11), 4103–4110 (2011)

    Article  Google Scholar 

  9. Adams, J.B., Smith, M.O., Johnson, P.E.: Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site. J. Geophys. Res. Atmos. 91, 8098–8112 (1986)

    Article  Google Scholar 

  10. 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(3), 650–663 (2004)

    Article  Google Scholar 

  11. Raksuntorn, Q., Du. N., Younan, N.H., King, R.L.: End-member extraction for hyperspectral image analysis. Appl. Opt. 47, 77–84 (2008)

    Article  Google Scholar 

  12. Keshava, N., Mustard, J.F.: Spectral unmixing. IEEE Signal Process. Mag. 19(1), 44–57 (2002)

    Article  Google Scholar 

  13. Borel, C.C., Gerstl, S.A.W.: Nonlinear spectral mixing model for vegetative and soil surfaces. Remote Sens. Environ. 47(3), 403–416 (1994)

    Article  Google Scholar 

  14. Raksuntorn, N., Du, Q.: Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment. IEEE Geosci. Remote Sens. Lett. 7(4), 836–840 (2010)

    Article  Google Scholar 

  15. Plaza, A., Martin, G., Plaza, J., Zortea, M., Sanchez, S.: Recent developments in spectral unmixing and endmember extraction. In: Prasad, S., Bruce, L.M., Chanussot, J. (eds.) Optical Remote Sensing, ch.12, pp. 235–267. Springer, Berlin (2011)

  16. Chang, C.-I., Du, Q.: Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 42(3), 608–619 (2004)

    Article  Google Scholar 

  17. Bioucas-Dias, J.M., Nascimento, J.M.P.: Hyperspectral subspace identification. IEEE Trans. Geosci. Remote Sens. 46(8), 2435–2445 (2008)

    Article  Google Scholar 

  18. Plaza, A., Chang, C.-I.: High Performance Computing in Remote Sensing. Taylor and Francis, Boca Raton (2007)

  19. Tarabalka, Y., Haavardsholm, T.V., Kasen, I., Skauli, T.: Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing. J. Real Time Image Process. 4, 1–14 (2009)

    Google Scholar 

  20. Plaza, A., Du, Q., Chang, Y.-L., King, R.L.: High performance computing for hyperspectral remote sensing. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 4(3), 528–544 (2011)

    Article  Google Scholar 

  21. 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(13), 1538–1557 (2011)

    Article  Google Scholar 

  22. 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., et al. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 65(3), 227–248 (1998)

    Article  Google Scholar 

  23. Winter, M.E,: N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proc. SPIE Image Spectr. V 3753, 266–277 (2003)

    Article  Google Scholar 

  24. Sanchez, S., Plaza, A.: Real-time implementation of a full hyperspectral unmixing chain on graphics processing units. Proc. SPIE Satell. Data Compress. Commun. Process. VII 8157, 1–9 (2011)

    Google Scholar 

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Authors and Affiliations

  1. Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politecnica de Cáceres, University of Extremadura, Cáceres, Spain

    Sergio Sánchez & Antonio Plaza

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  1. Sergio Sánchez
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  2. Antonio Plaza
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Correspondence to Antonio Plaza.

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Sánchez, S., Plaza, A. Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs. J Real-Time Image Proc 9, 397–405 (2014). https://doi.org/10.1007/s11554-012-0276-3

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