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

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Abstract

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.

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Notes

  1. http://speclab.cr.usgs.gov/wtc.

  2. http://pubs.usgs.gov/of/2001/ofr-01-0429/hotspot.key.tgif.gif.

  3. http://aviris.jpl.nasa.gov.

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

  5. http://speclab.cr.usgs.gov/cuprite.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., 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)

    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.A., 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.: 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)

    Article  MathSciNet  Google Scholar 

  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)

    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. Sel. Top. Appl. Earth Obs. Remote Sens. 4(3), 508–527 (2011)

    Article  Google Scholar 

  7. Keshava, N.: Spectral unmixing. IEEE Signal Process. Mag. 19(1), 44–57 (2002)

    Article  Google Scholar 

  8. Keshava, N.: A survey of spectral unmixing algorithms. Linc. Lab. J. 14(1), 55–78 (2003)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. Plaza, A., Plaza, J., Paz, A., Sanchez, S.: Parallel hyperspectral image and signal processing. IEEE Signal Process. Mag. 28, 119126 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  25. Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing, 2nd edn. Academic Press, New York (1997)

    Google Scholar 

  26. Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis: An Introduction. Springer, Berlin (2006)

    Google Scholar 

  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)

  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)

    Article  MATH  Google Scholar 

  29. Chang, C.-I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Kluwer/Plenum Publishers, New York (2003)

    Book  Google Scholar 

  30. Clint, M., Jenning, A.: The evaluation of eigenvalues and eigenvectors of real symmetric matrices by simultaneous iteration, Comput. J. 13, 76–80 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  31. Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn, pp. 406–408. Johns Hopkins University Press, Baltimore (1996)

  32. Parlett, B.N.: The Symmetric Eigenvalue Problem. Society for Industrial and Applied Mathematics (1998)

  33. Parlett, B.N., Dhillon, I.S.: Relatively robust representations of symmetric tridiagonals. Linear Algebra Appl. 309(1–3), 121–151 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  35. Saad, Y.: Numerical Methods for Large Eigenvalue Problems, revised edition. Society for Industrial and Applied Mathematics (2011)

  36. Craig, M.D.: Minimum-volume transforms for remotely sensed data. IEEE Trans. Geosci. Remote Sens. 32, 542552 (1994)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    MathSciNet  Google Scholar 

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Acknowledgments

This work was supported by national funds through FCT – Fundação para a Ciência e a Tecnologia, under project PEst-OE/EEI/LA0021/2011. This work has also been supported by the European Community’s Marie Curie Research Training Networks Programme under reference MRTN-CT-2006-035927, Hyperspectral Imaging Network (HYPER-I-NET), and by the Spanish Ministry of Science and Innovation (HYPERCOMP/EODIX project, reference AYA2008-05965-C04-02). Sergio Sánchez is sponsored by a research fellowship with reference PTA2009-2611-P, associated with the aforementioned project. Funding from Junta de Extremadura (local government) under project PRI09A110 is also gratefully acknowledged. Last but not least, we gratefully thank the Editor and the three anonymous reviewers for their outstanding comments and suggestions, which greatly helped us to improve the quality and presentation of this manuscript.

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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

  2. INESC-ID, IST, Technical University of Lisbon, Lisbon, Portugal

    Rui Ramalho & Leonel Sousa

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  1. Sergio Sánchez
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  2. Rui Ramalho
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  3. Leonel Sousa
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  4. Antonio Plaza
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Correspondence to Antonio Plaza.

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Sánchez, S., Ramalho, R., Sousa, L. et al. Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs. J Real-Time Image Proc 10, 469–483 (2015). https://doi.org/10.1007/s11554-012-0269-2

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