Optical Remote Sensing pp 9-29

Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 3)

Hyperspectral Data Compression Tradeoff

Chapter

Abstract

Hyperspectral data are a challenge for data compression. Several factors make the constraints particularly stringent and the challenge exciting. First is the size of the data: as a third dimension is added, the amount of data increases dramatically making the compression necessary at different steps of the processing chain. Also different properties are required at different stages of the processing chain with variable tradeoff. Second, the differences in spatial and spectral relation between values make the more traditional 3D compression algorithms obsolete. And finally, the high expectations from the scientists using hyperspectral data require the assurance that the compression will not degrade the data quality. All these aspects are investigated in the present chapter and the different possible tradeoffs are explored. In conclusion, we see that a number of challenges remain, of which the most important is to find an easier way to qualify the different algorithm proposals.

References

  1. 1.
    Abousleman, G., Lam, T.-T., Karam, L.: Robust hyperspectral image coding with channel-optimized trellis-coded quantization. IEEE Trans. Geosci. Remote Sens. 40(4), 820–830 (2002)CrossRefGoogle Scholar
  2. 2.
    Mielikainen, J., Toivanen, P.: Lossless compression of hyperspectral images using a quantized index to lookup tables. Geosci. Remote Sens. Lett. 5(3), 474–478 (2008)CrossRefGoogle Scholar
  3. 3.
    Huo, C., Zhang, R., Peng, T.: Lossless compression of hyperspectral images based on searching optimal multibands for prediction. Geosci. Remote Sens. Lett. 6(2), 339–343 (2009)CrossRefGoogle Scholar
  4. 4.
    Magli, E.: Multiband lossless compression of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 47(4), 1168–1178 (2009)CrossRefGoogle Scholar
  5. 5.
    Kiely, A.B., Klimesh, M.A.: Exploiting calibration-induced artifacts in lossless compression of hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 47(8), 2672–2678 (2009)CrossRefGoogle Scholar
  6. 6.
    Qian, S.-E., Bergeron, M., Cunningham, I., Gagnon, L., Hollinger, A.: Near lossless data compression onboard a hyperspectral satellite. IEEE Trans. Aerospace Electron. Syst. 42(3), 851–866 (2006)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Krishnamachari, S., Chellappa, R.: Multiresolution Gauss–Markov random field models for texture segmentation. IEEE Trans. Image Process. 39(2), 251–267 (1997)CrossRefGoogle Scholar
  9. 9.
    Bruce, L.M., Morgan, C., Larsen, S.: Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms. IEEE Trans. Geosci. Remote Sens. 39(10), 2217–2226 (2001)CrossRefGoogle Scholar
  10. 10.
    International charter: Space and major disasters. http://www.disasterscharter.org/main_e.html
  11. 11.
    Jet Propulsion Laboratory: AVIRIS free standard data product. http://aviris.jpl.nasa.gov/html/aviris.freedata.html
  12. 12.
    Zhang, J., Liu, G.: An efficient reordering prediction-based lossless compression algorithm for hyperspectral images. Geosci. Remote Sens. Lett. 4(2), 283–287 (2007)CrossRefGoogle Scholar
  13. 13.
    Aiazzi, B., Baronti, S., Alparone, L.: Lossless compression of hyperspectral images using multiband lookup tables. Geosci. Remote Sens. Lett. 16(6), 481–484 (2009)Google Scholar
  14. 14.
    Wang, H., Babacan, S.D., Sayood, K.: Lossless hyperspectral-image compression using context-based conditional average. IEEE Trans. Geosci. Remote Sens. 45(12), 4187–4193 (2007)CrossRefGoogle Scholar
  15. 15.
    Qian, S.-E.: Hyperspectral data compression using a fast vector quantization algorithm. IEEE Trans. Geosci. Remote Sens. 42(8), 1791–1798 (2004)CrossRefGoogle Scholar
  16. 16.
    Fowler, J.E., Rucker, J.T.: 3D wavelet-based compression of hyperspectral imagery. In: Chang, C.-I. (ed.) Hyperspectral Data Exploitation: Theory and Applications, Chapter 14, pp. 379–407. Wiley, Hoboken (2007)Google Scholar
  17. 17.
    Penna, B., Tillo, T., Magli, E., Olmo, G.: Progressive 3-D coding of hyperspectral images based on JPEG 2000. IEEE Geosci. Remote Sens. Lett. 3(1), 125–129 (2006)CrossRefGoogle Scholar
  18. 18.
    Christophe, E., Mailhes, C., Duhamel, P.: Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3D wavelet coding. IEEE Trans. Image Process. 17(12), 2334–2346 (2008)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Christophe, E., Pearlman, W.A.: Three-dimensional SPIHT coding of volume images with random access and resolution scalability. EURASIP J. Image Video Process. (2008). doi:10.1155/2008/248905
  20. 20.
    Cheung, N.-M., Wang, H., Ortega, A.: Sampling-based correlation estimation for distributed source coding under rate and complexity constraints. IEEE Trans. Image Process.17(11), 2122–2137 (2008)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Wang, L., Wu, J., Jiao, L., Shi, G.: Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT. IEEE Geosci. Remote Sens. Lett. 6(3), 587–591 (2009)CrossRefGoogle Scholar
  22. 22.
    García-Vílchez, F., Serra-Sagristà, J.: Extending the CCSDS recommendation for image data compression for remote sensing scenarios. IEEE Trans. Geosci. Remote Sens. 47(10), 3431–3445 (2009)CrossRefGoogle Scholar
  23. 23.
    Du, Q., Fowler, J.E., Zhu, W.: On the impact of atmospheric correction on lossy compression of multispectral and hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 47(1), 130–132 (2009)CrossRefGoogle Scholar
  24. 24.
    Zhang, J., Fowler, J.E., Liu, G.: Lossy-to-lossless compression of hyperspectral imagery using three-dimensional TCE and an integer KLT. IEEE Geosci. Remote Sens. Lett. 5(4), 814–818 (2008)CrossRefGoogle Scholar
  25. 25.
    Carvajal, G., Penna, B., Magli, E.: Unified lossy and near-lossless hyperspectral image compression based on JPEG 2000. IEEE Geosci. Remote Sens. Lett. 5(4), 593–597 (2008)CrossRefGoogle Scholar
  26. 26.
    Du, Q., Zhu, W., Fowler, J.E.: Anomaly-based JPEG2000 compression of hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 5(4), 696–700 (2008)CrossRefGoogle Scholar
  27. 27.
    Penna, B., Tillo, T., Magli, E., Olmo, G.: Hyperspectral image compression employing a model of anomalous pixels. IEEE Geosci. Remote Sens. Lett. 4(4), 664–668 (2007)CrossRefGoogle Scholar
  28. 28.
    Penna, B., Tillo, T., Magli, E., Olmo, G.: Transform coding techniques for lossy hyperspectral data compression. IEEE Trans. Geosci. Remote Sens. 45(5), 1408–1421 (2007)CrossRefGoogle Scholar
  29. 29.
    Tate, S.R.: Band ordering in lossless compression of multispectral image. IEEE Trans. Geosci. Remote Sens. 46(4), 477–483 (1997)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Mielikainen, J.: Lossless compression of hyperspectral images using lookup tables. IEEE Signal Process. Lett. 13(3), 157–160 (2006)CrossRefGoogle Scholar
  31. 31.
    Qian, S.-E., Hollinger, A., Williams, D., Manak, D.: Vector quantization using spectral index-based multiple subcodebooks for hyperspectral date compression. IEEE Trans. Geosci. Remote Sens. 38(3), 1183–1190 (2000)CrossRefGoogle Scholar
  32. 32.
    Motta, G., Rizzo, F., Storer, J.A.: Compression of hyperspectral imagery. In: Data Compression Conference, DCC, vol. 8. IEEE, Mar. 2003, pp. 333– 342Google Scholar
  33. 33.
    Ryan, M.J., Arnold, J.F.: Lossy compression of hyperspectral data using vector quantization. Remote Sens. Environ. 61, 419–436 (1997)CrossRefGoogle Scholar
  34. 34.
    Ryan, M., Pickering, M.: An improved M-NVQ algorithm for the compression of hyperspectral data. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2000, vol. 2, pp. 600–602 (2000)Google Scholar
  35. 35.
    Implementation du décorellateur multispectral—R&T Compression. Alcatel Alenia Space, Tech. Rep. 100137101A, Nov (2006)Google Scholar
  36. 36.
    Thiebaut, C., Christophe, E., Lebedeff, D., Latry, C.: CNES studies of on-board compression for multispectral and hyperspectral images. In: SPIE, Satellite Data Compression, Communications, and Archiving III, vol. 6683. SPIE, August (2007)Google Scholar
  37. 37.
    Penna, B., Tillo, T., Magli, E., Olmo, G.: A new low complexity KLT for lossy hyperspectral data compression. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS’06, August (2006), pp. 3525–3528Google Scholar
  38. 38.
    Liu, G., Zhao, F.: Efficient compression algorithm for hyperspectral images based on correlation coefficients adaptive 3D zerotree coding. IET Image Process. 2(2), 72–82 (2008)CrossRefMathSciNetGoogle Scholar
  39. 39.
    Christophe, E., Duhamel, P., Mailhes, C.: Adaptation of zerotrees using signed binary digit representations for 3 dimensional image coding. EURASIP J. Image Video Process. (2007)Google Scholar
  40. 40.
    Tang, X., Pearlman, W.A.: Scalable hyperspectral image coding. In IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP’05, vol. 2, pp. 401–404 (2005)Google Scholar
  41. 41.
    Cho, Y., Pearlman, W.A., Said, A.: Low complexity resolution progressive image coding algorithm: progres (progressive resolution decompression). In: IEEE International Conference on Image Processing, vol. 3, pp. 49–52 (2005)Google Scholar
  42. 42.
    Information technology—JPEG 2000 image coding system: Core coding system, ISO/IEC Std. 15 444-1 (2002)Google Scholar
  43. 43.
    Bowles, J., Gillis, D., Palmadesso, P.: New improvements in the ORASIS algorithm. Aerospace Conference Proceedings 3, 293–298 (2000)Google Scholar
  44. 44.
    Langevin, Y., Forni, O.: Image and spectral image compression for four experiments on the ROSETTA and Mars Express missions of ESA. In Applications of Digital Image Processing XXIII, vol. 4115. SPIE, 2000, pp. 364–373.Google Scholar
  45. 45.
    Yeh, P.-S., Armbruster, P., Kiely, A., Masschelein, B., Moury, G., Schaefer, C., Thiebaut, C.: The new CCSDS image compression recommendation. In IEEE Aerospace Conference. IEEE, March (2005)Google Scholar
  46. 46.
    Qian, S.-E., Hollinger, A., Bergeron, M., Cunningham, I., Nadeau, C., Jolly, G., Zwick, H.: A multi-disciplinary user acceptability study of hyperspectral data compressed using onboard near lossless vector quantization algorithm. Int. J. Remote Sens. 26(10), 2163–2195 (2005)CrossRefGoogle Scholar
  47. 47.
    Rast, M., Bezy, J.L., Bruzzi, S.: The ESA medium resolution imaging spectrometer MERIS - a review of the instrument and its mission. Int. J. Remote Sens.20(9), 1681–1702 (1999)CrossRefGoogle Scholar
  48. 48.
    Wang, Z., Bovik, A.C.: Mean square error: love it or leave it?. IEEE Signal Process. Mag. 26(1), 98–117 (2009)CrossRefGoogle Scholar
  49. 49.
    Christophe, E., Léger, D., Mailhes, C.: Quality criteria benchmark for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 43(09), 2103–2114 (2005)CrossRefGoogle Scholar
  50. 50.
    DFTC fusion contest (2008). http://tlclab.unipv.it/dftc/home.do?id=3
  51. 51.
    Licciardi, G., Pacifici, F., Tuia, D., Prasad, S., West, T., Giacco, F., Inglada, J., Christophe, E., Chanussot, J., Gamba, P.: Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S Data Fusion Contest. IEEE Trans. Geosci. Remote Sens. 47(11), 3857–3865 (2009)CrossRefGoogle Scholar
  52. 52.
    Information technology – JPEG 2000 image coding system: Extensions, ISO/IEC Std. 15 444-2, (2004)Google Scholar
  53. 53.
    Taubman, D.: Kakadu Software v 6.0 (2009). http://www.kakadusoftware.com/

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Centre for Remote Imaging, Sensing and ProcessingNational University of SingaporeSingaporeSingapore

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