TELECOMMUNICATIONS AND RADIO ENGINEERING - 2010 Vol. 69,
No 6 		 

 

 

 

An Automatic Approach to Lossy Compression of AVIRIS Hyperspectral Data


N.N. Ponomarenko, V.V. Lukin, M.S. Zriakhov

National Aerospace University (Kharkiv Aviation Institute),
17, Chkalov St., Kharkiv, 61070, Ukraine
Address all correspondence to V.V. Lukin E-mail: lukin@xai.kharkov.ua

A. Kaarna
Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland

J.T. Astola
Institute of Signal Processing, Tampere University of Technology,
P.O.Box 553, FIN-33101, Tampere, Finland

Abstract
In this paper we design and study an automatic approach to lossy compression of AVIRIS hyperspectral data. This approach takes into account the statistical characteristics of noise in component images as well as the considerable inter-channel correlation of data. An automatic method to estimate the noise variance in component images is proposed, and its performance is studied. It is shown that the accuracy of the variance estimates is appropriate for further use. Then, the selection of compression parameters of an advanced DCT-based coder, AGU, for component-wise (2-D) and group-wise (3-D) lossy compression of hyperspectral data is discussed. These two ways of lossy compression are compared for a set of standard AVIRIS images. We demonstrate that channel grouping with respect to estimated noise variances allows minimizing distortions and provides compression ratios which are approximately twice larger than in component-wise automatic lossy compression. It is shown that for AVIRIS images the achieved compression ratios can be of the order 7…29. Moreover, we demonstrate that for those subbands where noise is relatively intensive, noise attenuation is provided simultaneously with data compression whilst for other subband images no visible distortions are introduced by our lossy coder.

KEY WORDS: statistical characteristics, image, remote sencing



References
  1. Xiuping, Jia, Richards, J.A., Gessner, W, and Ricken, D.E., (1999), Remote Sensing Digital Image Analysis. An Introduction. Springer-Verlag, Berlin.
  2. Chein-I Chang (ed.) (2007), Hyperspectral Data Exploitation: Theory and Applications, Wiley-Interscience.
  3. Christophe, E., Leger, D., and Mailhes, C., (2005), Quality criteria benchmark for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 43(9):2103-2114.
  4. Toivanen, P., Kubasova, O., and Mielikainen, J., (2005), Correlation-based band-ordering heuristic for lossless compression of hyperspectral sounder data, IEEE Geoscience and Remote Sensing Letters, 2(1):50-54. 
  5. AVIRIS Home page, http://aviris.jpl.nasa.gov/, Accessed Jan 6, 2007.
  6. Pal, M.D., Brislawn, C.M., and Brumby, S.P., (2002), Feature extraction from hyperspectral images compressed using the JPEG-2000 standard, Proc. of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, pp.168-172.
  7. Aiazzi, B., Alparone, L., and Baronti, S., (2001), Near-lossless compression of 3-D optical data, IEEE Transactions on Geoscience and Remote Sensing, 39(11):2547-2557.
  8. Mielikäinen, J., (2006), Lossless compression of hyperspectral images using lookup tables, IEEE Signal Processing Letters, 13(3):157-160.
  9. Motta, G., Rizzo, F., and Storer, J.A., (2003), Compression of hyperspectral imagery, Proc. of Data Compression Conference, pp. 333-342.
  10. Kaarna, A., (2000), Multispectral image compression using the wavelet transform, Thesis for the Degree of Doctor of Science (Technology), University of Technology, Lappeenranta, Finland.
  11. Tang, X., Pearlman, W.A., and Modestino, J.W., Hyperspectral image compression using three-dimensional wavelet coding: a lossy-to-lossless solution, available from http://www.cipr.rpi.edu/~pearlman/papers/tgrs04_tpm.pdf
  12. Aiazzi, B., Baronti, S., Lastri, C., Santurri, L., and Alparone, L., (2005), Low complexity lossless/near-lossless compression of hyperspectral imagery through classified linear spectral prediction, Proc. of IGARSS, 4 p.
  13. Kaarna, A., Zemcik, P., Kalviainen, H., and Parkkinen, J., (2000), Compression of multispectral remote sensing images using clustering and spectral reduction, IEEE Transactions on Geoscience and Remote Sensing, GRS-38(2):1073-1082.
  14. Tang, X., and Pearlman, W.A., Three-dimensional wavelet-based compression of hyperspectral images, in: Hyperspectral Data Compression, Springer US.
  15. Miguel, A.C., Askew, A.R., Chang, A., Hauck, S., Ladner, R.E., and
    Riskin, E.A., (2004), Reduced complexity wavelet-based predictive coding of hyperspectral images for FPGA implementation, Proc. of Data Compression Conference, pp. 1-10.
  16. Ryan, M.J. and Pickering, M.R., (2000), An improved M-NVQ algorithm for the compression of hyperspectral data, Proc. of IGARSS, 2:600-602. 
  17. Du, Q. and Fowler, J.E., (2007), Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis, IEEE Trans. on Geoscience and Remote Sensing, 4(2):201-205.
  18. Kaarna, A., (2001), Integer PCA and wavelet transforms for multispectral image compression, Proc. of IGARSS, pp. 1853-1855.
  19. Cagnazzo, M., Poggi, G., Verdoliva, L., and Zinicola, A., (2004), Region-oriented compression of multispectral images by shape-adaptive wavelet transform and SPIHT, Proc. of ICIP, pp. 2459-2462.
  20. Penna, B., Tillo, T., Magli, E., and Olmo, G., (2006), Progressive 3-D Coding of Hyperspectral Images Based on JPEG2000, IEEE Geoscience and Remote Sensing Letters, 3(1):125-129.
  21. Al-Chaykh, O.K., and Mersereau, R.M., (1998), Lossy compression of noisy images, IEEE Transactions on Image Processing, 7(12):1641-1652.
  22. Chang, S.G., Yu, B., and Vetterli, M., (2000), Adaptive wavelet thresholding for image denoising and compression, IEEE Trans. on Image Processing, 9(9):1532-1546.
  23. Ponomarenko, N., Lukin, V., Zriakhov, M., Egiazarian, K., and Astola, J., (2006), Estimation of accesible quality in noisy image compression, Proc. of EUSIPCO, Florence, Italy, 4 p.
  24. Lukin, V., Ponomarenko, N., Zriakhov, M., Zelensky, A., Egiazarian, K., and
    Astola, J., (2006), Quasi-optimal compression of noisy optical and radar images, Proc. of SPIE Conf. “Image and Signal Processing for Remote Sensing XII”, Sweden, SPIE, 6365:63650N (SPIE Homepage).
  25. Lukin, V., Ponomarenko, N., Kurekin, A., Lever, K., Pogrebnyak, O., and
    Sanchez-Fernandez, L., (2006), Approaches to classification of multichannel images, Proc. of CIAPR, Cancun, Mexico, 4225:794-803.
  26. Ponomarenko, N.N., Lukin, V.V., and Zriakhov, M.S., (2005), Lossy compression of images with additive noise, Proc. of International Conference on Advanced Concepts for Intelligent Vision Systems, Belgium, Antwerpen, pp. 381-386.
  27. Ponomarenko, N.N., Lukin, V.V., Egiazarian, K., and Astola, J., (2005), DCT based high quality image compression, Proc. of 14-th Scandinavian Conference on Image Analysis, Joensuu, Finland, pp. 1177-1185.
  28. Barducci, A., Guzzi, D., Marcoinni, P., and Pippi, I., (2005), CHRIS-Proba performance evaluation: signal-to-noise ratio, instrument efficiency and data quality from acquisitions over San Rossore (Italy) test site, Proc. of the 3-rd ESA CHRIS/Proba, Workshop, Italy, 11 p.
  29. Carton-Vandecandelaere, M.P., Vozel, B., Klaine, L., and Chehdi, K., (2002), Application to multispectral images of a blind identification system for blur, additive, multiplicative and impulse noises, Proc. of EUSIPCO, pp. 283-286.
  30. Lukin, V., Ponomarenko, N., Abramov, S., Vozel, B., and Chehdi, K., (2007), Improved noise parameter estimation and filtering of MM-mand SLAR images, Proc. of MSMW, Kharkiv, Ukraine, pp. 439-441.
  31. Foi, A., Alenius, S., Katkovnik, V., and Egiazarian, K., (2007), Noise Measurement for Raw Data of Digital Imaging Sensors by Automatic Segmentation of Nonuniform Targets, IEEE Sensors Journal, 7(10):1456-1461.   
  32. Ponomarenko, N., Lukin, V., Zriakhov, M., and Kaarna, A., (2006), Preliminary automatic analysis of characteristics of hyperspectral AVIRIS images, Proc.of MMET, Kharkiv, Ukraine, pp. 158-160.
  33. Ponomarenko, N., Lukin, V., Zriakhov, M., Kaarna, A., and Astola, J., (2007), An automatic approach to lossy compression of AVIRIS images, Proc. of IGARSS, Barcelona, Spain, pp.472-475.
  34. Lukin, V.V., Abramov, S.K., Vozel, B., and Chehdi, B., (2005), A method for blind automatic evaluation of noise variance in images based on bootstrap and myriad operations, Proc. of SPIE/EUROPTO Symp. On Satellite Remote Sensing, Bruges, Belgium, pp. 299-310.
  35. Ponomarenko, N.N., Lukin, V.V., Abramov, S.K., Egiazarian, K., and Astola, J., (2003), Blind evaluation of additive noise variance in textured images by nonlinear processing of block DCT coefficients, Proc. of SPIE, pp. 178-189.
  36. Lukin, V.V., Abramov, S.K., Zelensky, A.A., and Astola, J.T., (2006), Use of minimal inter-quantile distance estimation in image processing, Proc. of SPIE, 6315:12 p.
  37. Curran, P.J., and Dungan, J.L., (1989), Estimation of signal-to-noise: a new procedure applied to AVIRIS data, IEEE Trans. on Geoscience and Remote Sensing, GRS-27(7):620-628.
  38. Oktem, R., Egiazarian, K., Lukin, V.V., Ponomarenko, N.N., and Tsymbal, O.V., (2007), Locally adaptive DCT filtering for signal-dependent noise removal, EURASIP Journal on Advances in Signal Processing, Article ID 42472 (open access paper), 10 p. 
  39. Du, Q. and Chang, Chein-I., (2004), Linear mixture analysis-based compression for hyperspectral image analysis, IEEE Trans. on Geoscience and Remote Sensing, 42:875-891.
  40. Lukin, V.V., Abramov, S.K., Ponomarenko, N.N., Vozel, B., and Chehdi, K., (2006), Methods for blind evaluation of noise variance in multichannel optical and radar images, Telecommunications and Radio Engineering, 65(6):509-537.
  41. Qjidaa, H. and Radouane, L., (1999), Robust line fitting in a noisy image by the method of moments, IEEE Trans. on Pattern Analysis and Machine Intelligence, 11:1216-1223.  
  42. Mallat, S., (1998), A Wavelet tour of signal processing, Academic Press, San Diego.
  43. Taubman, D., and Marcellin M., (2002), JPEG2000: Image Compression Fundamentals, Standards and Practice, Boston, Kluwer.
  44. Ponomarenko, N., Lukin, V., Zriakhov, M., and Pogrebnyak, O., (2006), Methods for Lossy Compression of Images Corrupted by Multiplicative Noise, Proc. of the International Conference “Modern Problems of Radioengineering, Telecommunications and Computer Science” (TCSET), L’viv-Slavsko, Ukraine, pp.278-281 (in Russian).
  45. Huang, H., Lin, X., Rahardja, S., and Yu, R., (2004), A method for realizing reversible type-IV discrete cosine transform (IntDCT-IV), Proc. of 7th International Conference on Signal Processing, pp. 101-104.
  46. Alam, M., Badawy, W., and Jullien, G., (2005), A New Time Distributed DCT Architecture for MPEG-4 Hardware Reference Model, IEEE Trans. on Circuits and Systems for Video Technology, 15(5):726-730.
  47. Penna, B., Tillo, T., Magli, E., and Olmo, G., (2007), Transform coding techniques for lossy hyperspectral data compression, IEEE Transactions on Geosciences and Remote Sensing, 45(5):1408-1421.


pages 537-563

Back