Advertisement

Towards Real-Time Compression of Hyperspectral Images Using Virtex-II FPGAs

  • Antonio Plaza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4641)

Abstract

Hyperspectral imagery is a new type of high-dimensional image data which is now used in many Earth-based and planetary exploration applications. Many efforts have been devoted to designing and developing compression algorithms for hyperspectral imagery. Unfortunately, most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop an FPGA-based data compression technique which relies on the concept of spectral unmixing, one of the most popular approaches to deal with mixed pixels and subpixel targets in hyperspectral analysis. The proposed method uses a two-stage approach in which the purest pixels in the image (endmembers) are first extracted and then used to express mixed pixels as linear combinations of end-members. The result is an intelligent, application-based compression technique which has been implemented and tested on a Xilinx Virtex-II FPGA.

Keywords

Hyperspectral Image Compression Algorithm Systolic Array Hyperspectral Data Mixed Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, C.-I.: Hyperspectral imaging: Detection & classification. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  2. 2.
    Motta, G., Rizzo, F., Storer, J.A.: Hyperspectral data compression. Springer, New York (2005)Google Scholar
  3. 3.
    Plaza, A., Chang, C.-I.: Impact of initialization on design of endmember extraction algorithms. IEEE Trans. Geoscience and Remote Sensing 44, 3397–3407 (2006)CrossRefGoogle Scholar
  4. 4.
    Plaza, A., Martinez, P., Perez, R., Plaza, J.: A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans. Geoscience and Remote Sensing 42, 650–663 (2004)CrossRefGoogle Scholar
  5. 5.
    Chang, C.-I., Plaza, A.: A Fast Iterative Implementation of the Pixel Purity Index Algorithm. IEEE Geoscience and Remote Sensing Letters 3, 63–67 (2006)CrossRefGoogle Scholar
  6. 6.
    Du, J., Chang, C.-I.: Linear Mixture Analysis-Based Compression for Hyperspectral Image Analysis. IEEE Trans. Geoscience and Remote Sensing 42, 875–891 (2004)CrossRefGoogle Scholar
  7. 7.
    El-Araby, E., El-Ghazawi, T., Le Moigne, J.: Wavelet spectral dimension reduction of hyperspectral imagery on a reconfigurable computer. In: Proc. of the 4th IEEE International Conference on Field-Programmable Technology, vol. 1, pp. 861–867 (2004)Google Scholar
  8. 8.
    Fry, T., Hauck, S.: Hyperspectral image compression on reconfigurable platforms. In: Proc. of the 10th IEEE Symposium on Field-Programmable Custom Computing Machines, vol. 1, pp. 305–312 (2002)Google Scholar
  9. 9.
    Plaza, A., Valencia, D., Plaza, J., Martinez, P.: Commodity cluster-based parallel processing of hyperspectral imagery. Journal of Parallel and Distributed Computing 66, 345–358 (2006)zbMATHCrossRefGoogle Scholar
  10. 10.
    Ramakhrishna, B., Plaza, A., Chang, C.-I., Ren, H.: Spectral/spatial hyperspectral image compression. In: Motta, G., Rizzo, F., Storer, J.A. (eds.) Hyperspectral data compression, pp. 309–346 (2005)Google Scholar
  11. 11.
    Valero-Garcia, M., Navarro, J., Llaberia, J., Valero, M., Lang, T.: A method for implementation of one-dimensional systolic algorithms with data contraflow using pipelined functional units. Journal of VLSI Signal Processing 4, 7–25 (1992)CrossRefGoogle Scholar
  12. 12.
    Zhang, D., Pal, S.K.: Neural Nets & Systolic Array Design. World Scientific (2002)Google Scholar
  13. 13.
    Dou, Y., Vassiliadis, S., Kuzmanov, G., Gaydadjiev, G.: 64-bit floating-point FPGA matrix multiplication. In: Proc. of the 13th ACM/SIGDA International Symposium on FPGAs, vol. 1, pp. 123–129 (2005)Google Scholar
  14. 14.
    Taubman, D.S., Marcellin, M.W.: JPEG2000: Image Compression Fundamentals, Standard and Practice. Kluwer, Boston (2002)Google Scholar
  15. 15.
    Said, A., Pearlman, W.A.: A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees. IEEE Transactions on Circuits and Systems 6, 243–350 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Antonio Plaza
    • 1
  1. 1.Department of Computer Science, University of Extremadura, Avda. de la Universidad s/n, E-10071 CaceresSpain

Personalised recommendations