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A Low-Rank Tensor Decomposition Based Hyperspectral Image Compression Algorithm

  • Mengfei Zhang
  • Bo Du
  • Lefei ZhangEmail author
  • Xuelong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

Hyperspectral image (HSI), which is widely known that contains much richer information in spectral domain, has attracted increasing attention in various fields. In practice, however, since a hyperspectral image itself contains large amount of redundant information in both spatial domain and spectral domain, the accuracy and efficiency of data analysis is often decreased. Various attempts have been made to solve this problem by image compression method. Many conventional compression methods can effectively remove the spatial redundancy but ignore the great amount of redundancy exist in spectral domain. In this paper, we propose a novel compression algorithm via patch-based low-rank tensor decomposition (PLTD). In this framework, the HSI is divided into local third-order tensor patches. Then, similar tensor patches are grouped together and to construct a fourth-order tensor. And each cluster can be decomposed into smaller coefficient tensor and dictionary matrices by low-rank decomposition. In this way, the redundancy in both the spatial and spectral domains can be effectively removed. Extensive experimental results on various public HSI datasets demonstrate that the proposed method outperforms the traditional image compression approaches.

Keywords

HSI Compression Reconstruction Tensor representation Low-rank decomposition 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grants 61401317, 61471274, 91338111, and U1536204.

References

  1. 1.
    Jia, X., Kuo, B.-C., Crawford, M.M.: Feature mining for hyperspectral image classification. Proc. IEEE 101(3), 676–697 (2013)CrossRefGoogle Scholar
  2. 2.
    Lee, S.H., Choi, J.Y., Ro, Y.M., Plataniotis, K.: Local color vector binary patterns from multichannel face images for face recognition. IEEE TIP 21(4), 2347–2353 (2012)MathSciNetGoogle Scholar
  3. 3.
    Chen, R., Ding, X.L.T.: Liveness detection for iris recognition using multispectral images. PR Lett. 33(12), 123–134 (2012)Google Scholar
  4. 4.
    Zhou, Y., Chang, H., Barner, K., Spellman, P., Parvin, B.: Classification of histology sections via multispectral convolutional sparse coding. In: Proceedings of CVPR, pp. 3081–3088 (2014)Google Scholar
  5. 5.
    Holloway, J., Priya, T., Veeraraghavan, A., Prasad, S.: Image classification in natural scenes: are a few selective spectral channels sufficient? In: Proceedings of ICIP, pp. 655–659 (2014)Google Scholar
  6. 6.
    Wallace, G.: The jpeg still picture compression standard. IEEE TCE 38(1), 18–34 (1992)Google Scholar
  7. 7.
    Said, A., Pearlman, W.A.: A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE TCSVT 6(6), 243–250 (1996)Google Scholar
  8. 8.
    Shapiro, J.M.: Embedded image coding using zero-trees of wavelet coefficients. IEEE TSP 41(12), 3445–3462 (1993)zbMATHGoogle Scholar
  9. 9.
    Du, Q., Fowler, J.E.: Hyperspectral image compression using jpeg2000 and principal component analysis. Geosci. Remote Sens. Lett. 4(2), 201–205 (2007)CrossRefGoogle Scholar
  10. 10.
    Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  11. 11.
    Guo, Y., Lin, X., Teng, Z., Xue, X., Fan, J.: A covariance-free iterative algorithm for distributed principal component analysis on vertically partitioned data. PR 45(3), 1211–1219 (2011)zbMATHGoogle Scholar
  12. 12.
    Wang, L., Jiao, L., Bai, J., Wu, J.: Hyperspectral image compression based on 3D reversible integer lapped transform, electron. Electron. Lett. 46(24), 1601–1602 (2010)CrossRefGoogle Scholar
  13. 13.
    Karami, A., Yazdi, M., Mercier, G.: Compression of hyperspectral images using discrete wavelet transform and tucker decomposition. IEEE JSTARS 5(2), 444–450 (2012)Google Scholar
  14. 14.
    Lathauwer, L.D.: Signal Processing Based on Multilinear Algebra. Katholieke Universiteit Leuven (1997)Google Scholar
  15. 15.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Mpca: multilinear principal component analysis of tensor objects. IEEE SPM 19(1), 18–39 (2008)Google Scholar
  16. 16.
    Xu, D., Yan, S., Zhang, L., Lin, S.: Reconstruction and recognition of tensor-based objects with concurrent subspaces analysis. IEEE TCSVT 18(1), 36–47 (2008)Google Scholar
  17. 17.
    Luo, Y., Tao, D., Wen, Y., Ramamohanarao, K., Xu, C., Wen, Y.: Tensor canonical correlation analysis for multi-view dimension reduction. IEEE TKDE 27(11), 3111–3124 (2015)Google Scholar
  18. 18.
    Zhang, L., Zhang, L., Tao, D., Huang, X., Du, B.: Compression of hyperspectral remote sensing images by tensor approach. Neurocomputing 147, 358–363 (2015)CrossRefGoogle Scholar
  19. 19.
    Zhang, L., Zhang, L., Tao, D., Huang, X.: Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction. IEEE TGRS 51(1), 242–256 (2013)Google Scholar
  20. 20.
    Yang, C., Shen, J., Peng, J., Fan, J.: Image collection summarization via dictionary learning for sparse representation. PR 46, 948–961 (2013)Google Scholar
  21. 21.
    Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: Proceedings of CVPR, pp. 60–65 (2005)Google Scholar
  22. 22.
    Zhou, C., Güney, F., Wang, Y., Geiger, A.: Exploiting object similarity in 3D reconstruction. In: Proceedings of ICCV (2015)Google Scholar
  23. 23.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: A survey of multilinear subspace learning for tensor data. PR 44(7), 1540–1551 (2011)zbMATHGoogle Scholar
  24. 24.
    Peng, Y., Meng, D., Xu, Z., Gao, C., Yang, Y., Zhang, B.: Decomposable nonlocal tensor dictionary learning for multispectral image denoising. In: Proceedings of CVPR, pp. 4321–4328 (2014)Google Scholar
  25. 25.
    Zhang, Z., Xu, Y., Yang, J., Li, X., Zhang, D.: A survey of sparse representation: algorithms and applications. CoRR abs, 3, 490–530 (2015)Google Scholar
  26. 26.
    Aharon, M., Elad, M., Bruckstein, A., Katz, Y.: K-SVD: an algorithm for designing over complete dictionaries for sparse representation. IEEE TSP 54(11), 4311–4322 (2006)Google Scholar
  27. 27.
    Wald, L.: Data Fusion: Definitions and Architectures: Fusion of Images of Different Spatial Resolutions. Les Presses Ecole des Mines, Paris (2002)Google Scholar
  28. 28.
    Yuhas, R.H., Boardman, J.W., Goetz, A.F.H.: Determination of semi-arid landscape endmembers and seasonal trends using convex geometry spectral unmixing techniques. Ratio 4(22) (1990)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina
  2. 2.Center for OPTIMAL, State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’anChina

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