Skip to main content
SpringerLink
Log in

Pairwise KLT-Based Compression for Multispectral Images

  • Original Paper
  • Published:
Sensing and Imaging Aims and scope Submit manuscript

Abstract

This paper presents a pairwise KLT-based compression algorithm for multispectral images. Although the KLT has been widely employed for spectral decorrelation, its complexity is high if it is performed on the global multispectral images. To solve this problem, this paper presented a pairwise KLT for spectral decorrelation, where KLT is only performed on two bands every time. First, KLT is performed on the first two adjacent bands and two principle components are obtained. Secondly, one remainning band and the principal component (PC) with the larger eigenvalue is selected to perform a KLT on this new couple. This procedure is repeated until the last band is reached. Finally, the optimal truncation technique of post-compression rate-distortion optimization is employed for the rate allocation of all the PCs, followed by embedded block coding with optimized truncation to generate the final bit-stream. Experimental results show that the proposed algorithm outperforms the algorithm based on global KLT. Moreover, the pairwise KLT structure can significantly reduce the complexity compared with a global KLT.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Lee, H. S., Younan, N. H., & King, R. L. (2002). Hyperspectral image cube compression combining JPEG-2000 and spectral decorrelation. In Proceedings of the international geoscience and remote sensing symposium (Vol. 6, pp. 3317–3319), Toronto, Canada.

  2. Gelli, G., & Poggi, G. (1999). Compression of multispectral images by spectral classification and transform coding. IEEE Transactions on Image Processing, 8(4), 476–489.

    Article  Google Scholar 

  3. Du, Q., & Fowler, J. E. (2007). Hyperspectral image compression using JPEG2000 and principal component analysis. IEEE Geoscience and Remote Sensing Letters, 4(2), 201–205.

    Article  Google Scholar 

  4. Wang, L., Wu, J., Jiao, L., & Shi, G. (2009). Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT. IEEE Geoscience and Remote Sensing Letters, 6(3), 587–591.

    Article  Google Scholar 

  5. Lee, C., Youn, S., Jeong, T., Lee, E., & Serra-Sagristà, J. (2015). Hybrid compression of hyperspectral images based on PCA with pre-encoding discriminant information. IEEE Geoscience and Remote Sensing Letters, 12(7), 1491–1495.

    Article  Google Scholar 

  6. Rucker, J. T., Fowler, J. E., & Younan, N. H. (2005). JPEG2000 coding strategies for hyperspectral data. In Proceedings of the international geoscience and remote sensing symposium (pp. 128–131), Seoul, South Korea.

  7. Bita, I. P. A., Barre, M., & Pham, D.-T. (2010). On optimal transforms in lossy compression of multicomponent images with JPEG2000. Signal Processing, 90, 759–773.

    Article  MATH  Google Scholar 

  8. Du, Q., Zhu, W., He, Y., & Fowler, J. E. (2009). Segmented principal component analysis for parallel compression of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 6(4), 713–717.

    Article  Google Scholar 

  9. Cheng, K.-J., & Dill, J. (2014). Lossless to lossy dual-tree BEZW compression for hyperspectral images. IEEE Transaction on Geoscience and Remote Sensing, 52(9), 5765–5770.

    Article  Google Scholar 

  10. Cagnazzo, M., Poggi, G., & Verdoliva, L. (2007). Region-based transform coding of multispectral images. IEEE Transactions on Image Processing, 16(12), 2916–2926.

    Article  MathSciNet  Google Scholar 

  11. Penna, B., Tillo, T., Magli, E., et al. (2007). Transform coding techniques for lossy hyperspectral data compression. IEEE Transactions on Geoscience and Remote Sensing, 45(5), 1408–1421.

    Article  Google Scholar 

  12. Blanes, I., & Serra-Sagrista, J. (2011). Pairwise orthogonal transform for spectral image coding. IEEE Transactions on Geoscience and Remote Sensing, 49(3), 961–972.

    Article  Google Scholar 

  13. Taubman, D. (2000). High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 9(7), 1158–1170.

    Article  Google Scholar 

Download references

Acknowledgments

This work has been sponsored by a grant from the National Natural Science Foundation of China (No. 41201363). The authors would like to thank Dr Fanqiang Kong for his assist for the revision of this article and those anonymous reviewers for their insightful comments, in improving the quality of this paper. Moreover, The images are CNES Copyright 2007 that are provided by CNES, distributed by SpotImage and produced by VITO.

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China

    Yongjian Nian & Yu Liu

  2. School of Electronics and Control Engineering, Chang’an University, Xi’an, 710064, China

    Zhen Ye

Authors
  1. Yongjian Nian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongjian Nian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nian, Y., Liu, Y. & Ye, Z. Pairwise KLT-Based Compression for Multispectral Images. Sens Imaging 17, 3 (2016). https://doi.org/10.1007/s11220-016-0128-5

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1007/s11220-016-0128-5

Keywords

Search

Navigation