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Multi-polarimetric SAR image compression based on sparse representation

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Abstract

The use of sparse representation in signal and image processing has gradually increased over the past few years. Obtaining an over-complete dictionary from a set of signals allows us to represent these signals as a sparse linear combination of dictionary atoms. By considering the relativity among the multi-polarimetric synthetic aperture radar (SAR) images, a new compression scheme for multi-polarimetric SAR image based sparse representation is proposed. The multilevel dictionary is learned iteratively in the 9/7 wavelet domain using a single channel SAR image, and the other channels are compressed by sparse approximation, also in the 9/7 wavelet domain, followed by entropy coding of the sparse coefficients. The experimental results are compared with two state-of-the-art compression methods: SPIHT (set partitioning in hierarchical trees) and JPEG2000. Because of the efficiency of the coding scheme, our method outperforms both SPIHT and JPEG2000 in terms of peak signal-to-noise ratio (PSNR) and edge preservation index (EPI).

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References

  1. Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Process, 1993, 41: 3445–3462

    Article  MATH  Google Scholar 

  2. Said A, Pearlman W A. A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circ Syst Video Technol, 1996, 6: 243–250

    Article  Google Scholar 

  3. Cumming V I, Wang J. Polarmetric SAR data compression using wavelet packets in a block coding scheme. In: IEEE International Geoscience and Remote Sensing Symposium, Toronto, 2002. 1126–1128

  4. Zhang W C, Wang Y F, Hu G H. Compression of multi-polarimetric SAR intensity images based on 3D-matrix transform. IET Image Process, 2008, 2: 194–202

    Article  Google Scholar 

  5. Skretting K, Engan K, Husoy J, et al. Sparse representation of images using overlapping frames. In: 12th Scandinavian Conference on Image Analysis, Bergen, 2001. 613–620

  6. Bryt O, Elad M. Compression of facial images using the K-SVD algorithm. J Visual Commun Image Represent, 2008, 19: 270–282

    Article  Google Scholar 

  7. Zepeda J, Guillemot C, Kijak E. Image compression using sparse representations and the iteration-tuned and aligned dictionary. IEEE J Sel Top Signal Process, 2010, (99): 1–1

  8. Skretting K, Engan K. Recursive least squares dictionary learning algorithm. IEEE Trans Signal Process, 2010, 58: 2121–2130

    Article  MathSciNet  Google Scholar 

  9. Skretting K, Engan K. Image compression using learned dictionaries by RLS-DLA and compared with K-SVD. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011. 1517–1520

  10. Davis G. Adaptive nonlinear approximations. Ph.D. dissertation. New York: New York University, 1994

    Google Scholar 

  11. Zhang S M Z, Mallat S. Matching pursuit with time-frequency dictionaries. IEEE Trans Signal Process, 1993, 41: 3397–3415

    Article  MATH  Google Scholar 

  12. Pati Y C, Rezaiifar R, Krishnaprasad P. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: Proc Asilomar Conference on Signals Systems and Computers, 1993. 40–44

  13. Gharavi-Alkhansari M, Huang T S. A fast orthogonal matching pursuit algorithm. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, 1998. 1389–1392

  14. Chen S, Wigger J. Fast orthogonal least squares algorithm for efficient subset model selection. IEEE Trans Image Process, 1995, 43: 1713–1715

    Google Scholar 

  15. Engan K, Skretting K, Husoy J H. A Family of iterative LS-based dictionary learning algorithms, ILS-DLA, for sparse signal representation. Digital Signal Process, 2007, 17: 32–49

    Article  Google Scholar 

  16. Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Image Process, 2006, 54: 4311–4322

    Google Scholar 

  17. Skretting K, Husøy J H, Aase S O. Improved Huffman coding using recursive splitting. In: NORSIG99, Asker, 1999. 92–95

  18. Sprljan N, Grgic S, Grgic M. Modified SPIHT algorithm for wavelet packet image coding. Real-Time Imag, 2005, 11: 378–388

    Article  Google Scholar 

  19. Hou X, Liu G, Zou Y. SAR image data compression using wavelet packet transform and universal-trellis coded quantization. IEEE Trans Geosc Rem Sens, 2004, 42: 2632–2641

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China

    Yuan Chen, Rong Zhang & Dong Yin

Authors
  1. Yuan Chen
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  2. Rong Zhang
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  3. Dong Yin
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Corresponding authors

Correspondence to Yuan Chen or Rong Zhang.

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Chen, Y., Zhang, R. & Yin, D. Multi-polarimetric SAR image compression based on sparse representation. Sci. China Inf. Sci. 55, 1888–1897 (2012). https://doi.org/10.1007/s11432-012-4612-9

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  • DOI: https://doi.org/10.1007/s11432-012-4612-9

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