Skip to main content
SpringerLink
Log in

Near-lossless and lossy compression of imaging spectrometer data: comparison of information extraction performance

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

We investigate the ability to derive meaningful information from decompressed imaging spectrometer data. Hyperspectral images are compressed with near-lossless and lossy coding methods. Linear prediction between the bands is used in both cases. Each band is predicted by a previously transmitted band. The residual is formed by subtracting the prediction from the original data and then is compressed either with a near-lossless bit-plane coder or with the lossy JPEG2000 algorithm. We study the effects of these two types of compression on hyperspectral image processing such as mineral and vegetation content classification using whole- and mixed pixel analysis techniques. The results presented in this paper indicate that an efficient lossy coder outperforms near-lossless method in terms of its impact on final hyperspectral data applications.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Motta, G., Storer, J. (eds): Hyperspectral Data Compression. Kluwer Academic Press, Boca Raton, FL (2005)

    Google Scholar 

  2. Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., Lastri, C., Santurri, L.: Near-lossless compression of hyperspectral data through classified spectral prediction. In: Proceedings of SPIE, Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications, vol. 5915, pp. 1–7 (2005)

  3. Aiazzi B., Alparone L., Baronti S.: Near-lossless compression of 3-D optical data. IEEE Trans. Geosci. Remote Sens. 39(11), 2547–2557 (2001)

    Article  Google Scholar 

  4. Aiazzi, B., Baronti, S., Lastri, C., Santurri, L., Alparone, L.: Low-complexity lossless/near-lossless compression of hyperspectral imagery through classified linear spectral prediction. In: Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS’05, vol. 1, p. 132 (2005)

  5. Aiazzi B., Alparone L., Baronti S., Lastri C.: Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 4(4), 532–536 (2007)

    Article  Google Scholar 

  6. Rizzo, F., Carpentieri, B., Motta, G., Storer, J.A.: High performance compression of hyperspectral imagery with reduced search complexity in the compressed domain. In: Proceedings Data Compression Conference, pp. 479–488 (2004)

  7. Magli E., Olmo G., Quacchio E.: Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC. IEEE Geosci. Remote Sens. Lett. 1(1), 21–25 (2004)

    Article  Google Scholar 

  8. Lucero, A., Cabrera, S., Vidal, E. Jr., Aguirre, A.: Evaluating residual coding with JPEG2000 for L-infinity driven hyperspectral image compression. In: Proceedings of SPIE, Satellite Data Compression, Communications, and Archiving, vol. 5889, pp. 1–12 (2005)

  9. Carvajal B.P.G., Magli E.: Unified lossy and near-lossless hyperspectral image compression based on JPEG 2000. IEEE Geosci. Remote Sens. Lett. 5(4), 593–597 (2008)

    Article  Google Scholar 

  10. Qian, S.-E., Hu, B., Bergeron, M., Hollinger, A., Oswald, P.: Quantitative evaluation of hyperspectral data compressed by near lossless onboard compression techniques. In: Proceedings of the 2002 IEEE International Geoscience and Remote Sensing Symposium, vol. 3, June, pp. 1425–1427 (2002)

  11. Qian S.-E., Hollinger A., Bergeron M., Cunningham I., Nadeau C., Jolly G., Zwick H.: A multidisciplinary user acceptability study of hyperspectral data compressed using an on-board near-lossless vector quantization algorithm. Int. J. Remote Sens. 26(10), 2163–2195 (2005)

    Article  Google Scholar 

  12. Qian S.-E., Bergeron M., Cunningham I., Gagnon L., Hollinger A.: Near lossless data compression onboard a hyperspectral satellite. IEEE Trans. Aerosp. Electron. Syst. 42(3), 851–866 (2006)

    Article  Google Scholar 

  13. Aiazzi, B., Alparone, L., Baronti, S., Lastri, C., Santurri, L., Selva, M.: Tradeoff between radiometric and spectral distortion in lossy compression of hyperspectral imagery. In: Proceedings of SPIE, Mathematics of Data/Image Coding, Compression, and Encryption VI, with Applications, vol. 5208, pp. 141–152 (2004)

  14. Penna, B., Tillo, T., Magli, E., Olmo, G.: Quality assessment for hyperspectral imagery: Comparison between lossy and near-lossless compression. In: Fortieth Asilomar Conference on Signals, Systems and Computers, pp. 1902–1906 (2006)

  15. Abousleman G.P., Marcellin M.W., Hunt B.R.: Hyperspectral image compression using entropy-constrained predictive trellis coded quantization. IEEE Trans. Image Process. 6(4), 566–573 (1997)

    Article  Google Scholar 

  16. Miguel, A., Askew, A., Chang, A., Hauck, S., Ladner, R., Riskin, E.: Reduced complexity wavelet-based predictive coding of hyperspectral images for FPGA implementation. In: Proceedings of the Data Compression Conference, 2004. DCC, pp. 469–478. IEEE (2004)

  17. Miguel, A., Liu, J., Barney, D., Ladner, R., Riskin, E.: Near-lossless compression of hyperspectral images. In: 2006 IEEE International Conference on Image Processing, pp. 1153–1156 (2006)

  18. Laboratory, J.P.: AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) homepage. http://aviris.jpl.nasa.gov/

  19. Shippert P.: Why use hyperspectral imagery? Photogrammetric engineering & remote sensing. J. Am. Soc. Photogramm. Remote Sens. 70(4), 377–380 (2004)

    Google Scholar 

  20. Global Services Group: ITT Visual Information Solutions, Spectral Analysis With ENVI, ser. ENVI Training Series. ITT (2006)

  21. Borengasser, M., Hungate, W.S., Watkins, R.: Hyperspectral Remote Sensing: Principles and Applications, ser. Taylor & Francis Series in Remote Sensing Applications. In: Weng, Q. (ed.) CRC Press, Boca Raton, FL (2008)

  22. Reed I., Yu X.: Adaptive multiple-band cfar detection of and optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 38(10), 1760–1770 (1990)

    Article  Google Scholar 

  23. Chen K., Ramabadran T.: Near-lossless compression of medical images through entropy-coded DPCM. IEEE Trans. Med. Imaging 13(3), 538–548 (1994)

    Article  Google Scholar 

  24. Das, M., Neuhoff, D.L., Lin, C.L.: Near-lossless compression of medical images. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 2347–2350 (1995)

  25. Ke L., Marcellin M.W.: Near-lossless image compression: Minimum-entropy, constrained-error DPCM. IEEE Trans. Image Process. 7(2), 225–228 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  26. Weinberger M.J., Seroussi G., Guillermo Sapiro M.: The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Trans. Image Process. 9(8), 1309–1324 (2000)

    Article  Google Scholar 

  27. Wu X., Memon N.: Context-based, adaptive, lossless image codec. IEEE Trans. Commun. 45(4), 437–444 (1997)

    Article  Google Scholar 

  28. Wu X., Bao P.: L-infinity-constrained high-fidelity image compression via adaptive context modeling. IEEE Trans. Image Process. 9(4), 536–542 (2000)

    Article  MATH  Google Scholar 

  29. Aiazzi, B., Baronti, S., Alparone, L.: Near lossless image compression by relaxation labeled prediction. In: Proceedings of the IEEE International Conference on Image Processing, pp. 148–151 (2000)

  30. Aiazzi, B., Baronti, S., Alparone, L.: Near-lossless compression of coherent image data. In: Proceedings of the IEEE International Conference on Image Processing, pp. 490–493 (2001)

  31. Aiazzi B., Alparone L., Baronti S.: Context modeling for near-lossless image coding. IEEE Signal Process. Lett. 9(3), 77–80 (2002)

    Article  Google Scholar 

  32. Ansari R., Memon N., Ceran E.: Near-lossless image compression techniques. J. Electron. Imaging 7(3), 486–494 (1998)

    Article  Google Scholar 

  33. Wu, X., Bao, P.: Near-lossless image compression by combining wavelets and CALIC. In: Conference Record of the 31st Asilomar Conference on Signals, Systems and Computers, pp. 1427–1431 (1998)

  34. Krivoulets, A.: A method for progressive near-lossless image compression. In: Proceedings of the IEEE International Conference on Image Processing, pp. 185–188 (2003)

  35. Yea, S., Pearlman, W.A.: A wavelet-based two-stage near-lossless coder. In: Proceedings of the IEEE International Conference on Image Processing, pp. 2503–2506 (2004)

  36. Alecu A., Munteanu A., Schelkens P., Cornelis J., Dewitte S.: Wavelet-based L-infinite scalable coding. Electron. Lett. 38(22), 1338–1340 (2002)

    Article  Google Scholar 

  37. Alecu A., Munteanu A., Cornelis J., Dewitte S., Schelkens P.: On the optimality of embedded deadzone scalar-quantizers for wavelet-based L-infinite-constrained image coding. IEEE Signal Process. Lett. 11(3), 367–370 (2004)

    Article  Google Scholar 

  38. Tate S.R.: Band ordering in lossless compression of multispectral images. IEEE Trans. Comput. 46(4), 477–483 (1997)

    Article  MathSciNet  Google Scholar 

  39. Cormen T.H., Leiserson C.E., Rivest R.L., Stein C.: Introduction to Algorithms. 2nd edn. McGraw-Hill, New York (2001)

    MATH  Google Scholar 

  40. Global Services Group, ITT Visual Information Solutions, ENVI Tutorial: Selected Hyperspectral Mapping Methods. ITT

  41. Global Services Group, ITT Visual Information Solutions, ENVI Tutorial: Vegetation Hyperspectral Analysis. ITT

  42. Global Services Group, ITT Visual Information Solutions, ENVI Tutorial: Near-Shore Marine Hyperspectral Analysis. ITT

  43. Plaza, A., Chang, C.-I.: An improved N-FINDR algorithm in implementation. In: Proceedings of SPIE, vol. 5806. The International Society for Optical Engineering, pp. 298–306 (2005)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Seattle University, 901 12th Avenue, P.O. Box 222000, Seattle, WA, 98122-1090, USA

    Agnieszka Miguel

  2. Department of Electrical Engineering, University of Washington, Seattle, WA, USA

    Eve Riskin

  3. Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Richard Ladner

  4. Double Negative, London, UK

    Dane Barney

Authors
  1. Agnieszka Miguel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eve Riskin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard Ladner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dane Barney
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Agnieszka Miguel.

Additional information

Research supported by National Science Foundation grant number CCR-0104800 and the University of Washington College of Engineering. Richard Ladner was supported in part by the Boeing Professorship in Computer Science and Engineering.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Miguel, A., Riskin, E., Ladner, R. et al. Near-lossless and lossy compression of imaging spectrometer data: comparison of information extraction performance. SIViP 6, 597–611 (2012). https://doi.org/10.1007/s11760-010-0191-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-010-0191-7

Keywords

Search

Navigation