Skip to main content
SpringerLink
Log in

Lookup-Table Based Hyperspectral Data Compression

  • Chapter
  • First Online:
Satellite Data Compression

Abstract

This chapter gives an overview of the lookup table (LUT) based lossless compression methods for hyperspectral images. The LUT method searches the previous band for a pixel of equal value to the pixel co-located to the one to be coded. The pixel in the same position as the obtained pixel in the current band is used as the predictor. Lookup tables are used to speed up the search. Variants of the LUT method include predictor guided LUT method and multiband lookup tables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Bilgin, G. Zweig, and M. Marcellin, “Three-dimensional image compression with integer wavelet transforms,” Appl. Opt., vol. 39, no. 11, pp. 1799–1814, Apr. 2000.

    Article  Google Scholar 

  2. B. Baizert, M. Pickering, and M. Ryan, “Compression of hyperspectral data by spatial/spectral discrete cosine transform,” in Proc. Int. Geosci. Remote Sens. Symp., 2001, vol. 4, pp. 1859–1861, doi: 10.1109/IGARSS.2001.977096.

    Google Scholar 

  3. J. Mielikainen and A. Kaarna, “Improved back end for integer PCA and wavelet transforms for lossless compression of multispectral images,” in Proc. 16th Int. Conf. Pattern Recog., Quebec City, QC, Canada, 2002, pp. 257–260, doi: 10.1109/ICPR.2002.1048287.

  4. M. Ryan and J. Arnold, “The lossless compression of AVIRIS images vector quantization,” IEEE Trans. Geosci. Remote Sens., vol. 35, no. 3, pp. 546–550, May 1997, doi: 10.1109/36.581964.

    Article  Google Scholar 

  5. J. Mielikainen and P. Toivanen, “Improved vector quantization for lossless compression of AVIRIS images,” in Proc. XI Eur. Signal Process. Conf., Toulouse, France, Sep. 2002, pp. 495–497.

    Google Scholar 

  6. G. Motta, F. Rizzo, and J. Storer, “Partitioned vector quantization application to lossless compression of hyperspectral images,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Jul. 2003, vol. 1, pp. 553–556, doi: 10.1109/ICME.2003.1220977.

  7. S. Tate, “Band ordering in lossless compression of multispectral images,” IEEE Trans. Comput., vol. 46, no. 4, pp. 477–483, Apr. 1997, doi: 10.1109/12.588062.

    Article  MathSciNet  Google Scholar 

  8. P. Toivanen, O. Kubasova, and J. Mielikainen, “Correlation-based bandordering heuristic for lossless compression of hyperspectral sounder data,” IEEE Geosci. Remote Sens. Lett., vol. 2, no. 1, pp. 50–54, Jan. 2005, doi: 10.1109/LGRS.2004.838410.

    Article  Google Scholar 

  9. J. Zhang and G. Liu, “An efficient reordering prediction based lossless compression algorithm for hyperspectral images,” IEEE Geosci. Remote Sens. Lett., vol. 4, no. 2, pp. 283–287, Apr. 2007, doi: 10.1109/LGRS.2007.890546.

    Article  Google Scholar 

  10. A. Abrardo, M. Barni, E. Magli, F. Nencini, “Error-Resilient and Low-Complexity On-board Lossless Compression of Hyperspectral Images by Means of Distributed Source Coding,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 1892–1904, 2010, doi:10.1109/TGRS.2009.2033470.

    Article  Google Scholar 

  11. A. B. Kiely, M. A. Klimesh, “Exploiting Calibration-Induced Artifacts in Lossless Compression of Hyperspectral Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2672–2678, 2009, doi:10.1109/TGRS.2009.2015291.

    Article  Google Scholar 

  12. M. Slyz, L. Zhang, “A block-based inter-band lossless hyperspectral image compressor,” in Proc. of IEEE Data Compression Conference, pp. 427–436, 2005, doi: 10.1109/DCC.2005.1.

  13. C.-C. Lin, Y.-T. Hwang., “An Efficient Lossless Compression Scheme for Hyperspectral Images Using Two-Stage Prediction”, vol. 7, no. 3, pp. 558–562, 2010, doi:10.1109/LGRS.2010.2041630.

  14. J. Mielikainen, P. Toivanen, “Clustered DPCM for the Lossless Compression of Hyperspectral Images”, IEEE Trans. Geosci. Remote Sens., vol. 41, no. 12, pp. 2943–2946, 2003 doi:10.1109/TGRS.2003.820885.

    Article  Google Scholar 

  15. B. Aiazzi, L. Alparone, S. Baronti, and C. Lastri, “Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery,” IEEE Geosci. Remote Sens. Lett., vol. 4, no. 4, pp. 532–536, Oct. 2007, 10.1109/LGRS.2007.900695.

    Article  Google Scholar 

  16. E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett., vol. 1, no. 1, pp. 21–25, Jan. 2004, doi:10.1109/LGRS.2003.822312.

    Article  Google Scholar 

  17. F. Rizzo, B. Carpentieri, G. Motta, and J. Storer, “Low-complexity lossless compression o hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett., vol. 12, no. 2, pp. 138–141, Feb. 2005, doi:10.1109/LSP.2004.840907.

    Article  Google Scholar 

  18. J. Mielikainen and P. Toivanen, “Parallel implementation of linear prediction model for lossless compression of hyperspectral airborne visible infrared imaging spectrometer images,” J. Electron. Imaging, vol. 14, no. 1, pp. 013010-1–013010-7, Jan.–Mar. 2005, doi:10.1117/1.1867998.

    Article  Google Scholar 

  19. H. Wang, S. Babacan, and K. Sayood, “Lossless Hyperspectral-Image Compression Using Context-Based Conditional Average,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 12, pp. 4187–8193, Dec. 2007, doi:0.1109/TGRS.2007.906085.

    Article  Google Scholar 

  20. M. Slyz and L. Zhang, “A block-based inter-band lossless hyperspectral image compressor,” in Proc. Data Compression Conf., Snowbird, UT, 2005, pp. 427–436, doi:10.1109/DCC.2005.1.

  21. S. Jain and D. Adjeroh, “Edge-based prediction for lossless compression of hyperspectral images,” in Proc. Data Compression Conf., Snowbird, UT, 2007, pp. 153–162, doi:10.1109/DCC.2007.36.

  22. J. Mielikainen, “Lossless compression of hyperspectral images using lookup tables,” IEEE Sig. Proc. Lett., vol. 13, no. 3, pp. 157–160, 2006, doi:10.1109/LSP.2005.862604.

    Article  Google Scholar 

  23. E. Magli, “Multiband lossless compression of hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 4, pp. 1168–1178, Apr. 2009, doi:10.1109/TGRS.2008.2009316.

    Article  Google Scholar 

  24. J. Mielikainen, P. Toivanen, “Lossless Compression of Hyperspectral Images Using a Quantized Index to Lookup Tables,” vol. 5, no. 3, pp. 474–477, doi:10.1109/LGRS.2008.917598.

  25. J. Mielikainen, P. Toivanen, and A. Kaarna, “Linear prediction in lossless compression of hyperspectral images,” Opt. Eng., vol. 42, no. 4, pp. 1013–1017, Apr. 2003, doi:10.1117/1.1557174.

    Article  Google Scholar 

  26. B. Aiazzi, S. Baronti, S., L. Alparone, “Lossless Compression of Hyperspectral Images Using Multiband Lookup Tables,” IEEE Signal Processing Letters, vol. 16, no. 6, pp. 481–484. Jun. 2009, doi:10.1109/LSP.2009.2016834, 0.1109/LSP.2009.2016834.

  27. W. Porter and H. Enmark, “A system overview of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Proc. SPIE, vol. 834, pp. 22–31, 1997

    Google Scholar 

  28. X. Wu and N. Memon, “Context-based lossless interband compression—Extending CALIC,” IEEE Trans. Image Process., vol. 9, no. 6, pp. 994–1001, Jun. 2000, doi:10.1109/83.846242.

    Article  Google Scholar 

  29. B. Huang, Y. Sriraja, “Lossless compression of hyperspectral imagery via lookup tables with predictor selection,” in Proc. SPIE, vol. 6365, pp. 63650L.1–63650L.8, 2006, doi:10.1117/12.690659.

    Google Scholar 

Download references

Acknowledgement

This work was supported by the Academy of Finland.

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea

    Jarno Mielikainen

Authors
  1. Jarno Mielikainen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jarno Mielikainen .

Editor information

Editors and Affiliations

  1. , Space Science and Engineering Center, University of Wisconsin - Madison, Madison, 53706, Wisconsin, USA

    Bormin Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Mielikainen, J. (2012). Lookup-Table Based Hyperspectral Data Compression. In: Huang, B. (eds) Satellite Data Compression. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1183-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-1183-3_8

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-1182-6

  • Online ISBN: 978-1-4614-1183-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation