Hyperspectral Image Compression Algorithms—A Review

  • K. Subhash Babu
  • V. Ramachandran
  • K. K. Thyagharajan
  • Geeta Santhosh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)

Abstract

Satellite-based remote sensing applications require collection of high volumes of image data of which hyperspectral images are a particular type. Hyperspectral images are collected by high-resolution instruments over a very large number of wavelengths on board a satellite/airborne vehicle and then sent onwards to a ground station for further processing. Compression of hyperspectral images is undertaken to reduce the on-board memory requirement, communication channel capacity, and the download time. Compression algorithms can be either lossless or lossy. The purpose of this paper is to review a number of compression techniques employed for onsite processing of hyperspectral image data, to reduce the transmission overhead. A review of the theory of hyperspectral images and the compression techniques employed therein with emphasis on recent research developments is presented. Recent research on video compression techniques for hyperspectral imaging (HSI) is also discussed.

Keywords

Hyperspectral imaging Compression Lossless Lossy 

References

  1. 1.
    Canada Centre for Remote Sensing, Fundamentals of Remote Sensing. Remote Sensing Tutorial (2013)Google Scholar
  2. 2.
  3. 3.
    P. Wayner, Data Compression for Real Programmers (Morgan Kaufman Inc., Los Altos, 1999)Google Scholar
  4. 4.
    K. Wang, L. Wang, H. Liao, J. Song, Y. Li, Lossless compression of hyperspectral images using adaptive edge-based prediction. Proceedings of SPIE 8871, satellite data compression, communications, and processing IX, 887105 (2013). doi: 10.1117/12.2022426
  5. 5.
    Q. Zhang, V.P. Pauca, R. Plemmons, Randomized methods in lossless compression of hyperspectral data. J. Appl. Remote Sens. 7(1), 074599–1 (2013)CrossRefGoogle Scholar
  6. 6.
    K.-J. Cheng, J. Dill, Hyperspectral images lossless compression using the 3D binary EZW algorithm. in Proceedings of SPIE 8655, Image Processing: algorithms and Systems XI, vol. 8655, ed. by K.O. Egiazarian, S.S. Agaian, A.P. Gotchev (Burlingame, California, 2013), p. 8655. doi: 10.1117/12.2002820
  7. 7.
    N. Rizuan, M. Noor, T. Vladimirova, Parallelised fault-tolerant integer KLT implementation for lossless hyperspectral image compression on board satellites. 2013 NASA/ESA conference on adaptive hardware and systems (AHS 2013)Google Scholar
  8. 8.
    C. Li, K. Guo, Lossless compression of hyperspectral images using interband gradient adjusted prediction. 4th IEEE international conference on software engineering and service science (ICSESS 2013), 23–25 May 2013. ISBN:978-1-4673-5000-6/13. doi: 10.1109/ICSESS2013.6615408
  9. 9.
    J. Song, Z. Zhang, X. Chen, Lossless compression of hyperspectral imagery via RLS filter. Electron. Lett. 49(16) (2013)Google Scholar
  10. 10.
    J. Mielikainen, Lossless compression of hyperspectral images using lookup tables. IEEE Signal Process Lett. 13(3), 157–160 (2006)Google Scholar
  11. 11.
    L. Bai, M. He, Y. Dai, Lossless compression of hyperspectral images based on 3D context prediction. 3rd IEEE conference on industrial electronics and applications (2008), pp. 1845–1848. ISBN:978-1-4244-1717-9Google Scholar
  12. 12.
    A.B. Kiely, M.A. Klimesh, Exploiting calibration-induced artifacts in lossless compression of hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 47(8), 2672–2678 (2009)CrossRefGoogle Scholar
  13. 13.
    L. Santos, E. Magli, R. Vitulli, A. Núñez, J.F. López, R. Sarmiento, Lossy hyperspectral image compression on a graphics processing unit: parallelization strategy and performance evaluation. J. Appl. Remote Sens. doi: 10.1117/1.JRS.7.074599
  14. 14.
    L. Santos, E. Magli, R. Vitulli, J.F. López, R. Sarmiento, Highly-parallel GPU architecture for lossy hyperspectral image compression. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sen. 6(2), 670–681 (2013)Google Scholar
  15. 15.
    B. Penna, T. Tillo, E. Magli, G. Olmo, Hyperspectral image compression employing a model of anomalous pixels. IEEE Geosci. Remote Sens. Lett. 4(4), 664 (2007)CrossRefGoogle Scholar
  16. 16.
    Y. Nian, M. He, J. Wan, Low-complexity compression algorithm for hyperspectral images based on distributed source coding. Math. Probl. Eng., Article ID 825673 (2013)Google Scholar
  17. 17.
    T.S. Wilkinson, V.D. Vaughn, Application of video based coding to hyperspectral imagery. Proceedings of SPIE 2821, hyperspectral remote sensing and applications, vol. 44 (1996). doi: 10.1117/12.257183
  18. 18.
    Z. Xiong, Video compression based on distributed source coding principles. Conference record of the forty-third Asilomar conference on signals, systems and computers (2009)Google Scholar
  19. 19.
    L. Santos, G.M. Callicó, J.F. Lopez, R. Sarmiento, Performance evaluation of the H.264/AVC video coding standard for lossy hyperspectral image compression. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 451–461 (2012). doi: 10.1109/JSTARS.2011.2173906

Copyright information

© Springer India 2015

Authors and Affiliations

  • K. Subhash Babu
    • 1
  • V. Ramachandran
    • 2
  • K. K. Thyagharajan
    • 3
  • Geeta Santhosh
    • 4
  1. 1.CSE DepartmentJawaharlal Nehru Technological University-KakinadaKakinadaIndia
  2. 2.RMD Engineering CollegeKavaraipettaiIndia
  3. 3.NITDimapurIndia
  4. 4.MCA DepartmentSSN College of Engineering KalavakkamChennaiIndia

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