Skip to main content
SpringerLink
Log in

Remote Sensing Image Fusion Method Based on Nonsubsampled Shearlet Transform and Sparse Representation

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

Abstract

The remote sensing image fusion is an important preprocessing technique in remote sensing image processing. In this paper, a remote sensing image fusion method based on the nonsubsampled shearlet transform (NSST) with sparse representation (SR) is proposed. Firstly, the low resolution multispectral (MS) image is upsampled and color space is transformed from Red–Green–Blue (RGB) to Intensity–Hue–Saturation (IHS). Then, the high resolution panchromatic (PAN) image and intensity component of MS image are decomposed by NSST to high and low frequency coefficients. The low frequency coefficients of PAN and the intensity component are fused by the SR with the learned dictionary. The high frequency coefficients of intensity component and PAN image are fused by local energy based fusion rule. Finally, the fused result is obtained by performing inverse NSST and inverse IHS transform. The experimental results on IKONOS and QuickBird satellites demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than other remote sensing image fusion methods both in visual effect and object evaluation.

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

Similar content being viewed by others

References

  1. Choi, M. (2006). A new intensity–hue–saturation fusion approach to image fusion with a tradeoff parameter. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1672–1682. doi:10.1109/TGRS.2006.869923.

    Article  Google Scholar 

  2. Rahmani, S., Strait, M., Merkurjev, D., Moeller, M., & Wittman, T. (2010). An Adaptive IHS Pan-Sharpening method. IEEE Geoscience and Remote Sensing Letters, 7(4), 746–750. doi:10.1109/lgrs.2010.2046715.

    Article  Google Scholar 

  3. Shah, V. P., Younan, N. H., & King, R. L. (2007). An adaptive PCA-based approach to pan-sharpening. In Image and signal processing for remote sensing XIII, Florence, Italy, 2007 (Vol. 6748, p. 674802). International Society for Optics and Photonics. doi:10.1117/12.736674.

  4. Aiazzi, B., Baronti, S., Selva, M., & Alparone, L. (2006). Enhanced Gram-Schmidt Spectral Sharpening Based on Multivariate Regression of MS and Pan Data. In 2006 IEEE international sensing symposium geoscience and remote, 31 July 2006–4 Aug 2006 (pp. 3806–3809). doi:10.1109/IGARSS.2006.975.

  5. Eshtehari, A., & Ebadi, H. (2008). Image fusion of landsat ETM+ and spot satellite images using IHS, Brovey and PCA. Tehran: Toosi University of Technology.

    Google Scholar 

  6. Alparone, L., Wald, L., Chanussot, J., Thomas, C., Gamba, P., & Bruce, L. M. (2007). Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data-fusion contest. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3012–3021. doi:10.1109/tgrs.2007.904923.

    Article  Google Scholar 

  7. Garzelli, A., & Nencini, F. (2006). PAN-sharpening of very high resolution multispectral images using genetic algorithms. International Journal of Remote Sensing, 27(15), 3273–3292. doi:10.1080/01431160600554991.

    Article  Google Scholar 

  8. Otazu, X., Gonzalez-Audicana, M., Fors, O., & Nunez, J. (2005). Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing, 43(10), 2376–2385. doi:10.1109/TGRS.2005.856106.

    Article  Google Scholar 

  9. Zheng, S., Shi, W. Z., Liu, H., & Tian, J. W. (2008). Remote sensing image fusion using multiscale mapped LS-SVM. IEEE Transactions on Geoscience and Remote Sensing, 46(5), 1313–1322. doi:10.1109/Tgrs.2007.912737.

    Article  Google Scholar 

  10. Shah, V. P., Younan, N. H., & King, R. (2007). Pan-sharpening via the contourlet transform. In IEEE international geoscience and remote sensing symposium, 23–28 Jul 2007 (pp. 310–313). doi:10.1109/IGARSS.2007.4422792.

  11. Choi, M., Kim, R. Y., Nam, M. R., & Kim, H. O. (2005). Fusion of multispectral and panchromatic satellite images using the curvelet transform. IEEE Geoscience and Remote Sensing Letters, 2(2), 136–140. doi:10.1109/lgrs.2005.845313.

    Article  Google Scholar 

  12. Li, S., & Yang, B. (2011). A new pan-sharpening method using a compressed sensing technique. IEEE Transactions on Geoscience and Remote Sensing, 49(2), 738–746. doi:10.1109/tgrs.2010.2067219.

    Article  Google Scholar 

  13. Fasbender, D., Radoux, J., & Bogaert, P. (2008). Bayesian data fusion for adaptable image pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 46(6), 1847–1857. doi:10.1109/tgrs.2008.917131.

    Article  Google Scholar 

  14. Easley, G., Labate, D., & Lim, W.-Q. (2008). Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis, 25(1), 25–46. doi:10.1016/j.acha.2007.09.003.

    Article  MathSciNet  MATH  Google Scholar 

  15. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12), 3736–3745. doi:10.1109/TIP.2006.881969.

    Article  MathSciNet  Google Scholar 

  16. Choi, M. J., Kim, H. C., Cho, N. I., & Kim, H. O. (2008). An improved intensity–hue–saturation method for IKONOS image fusion. International Journal of Remote Sensing (in pre-print).

  17. Tu, T. M., Huang, P. S., Hung, C. L., & Chang, C. P. (2004). A fast IHS fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1(4), 309–312. doi:10.1109/lgrs.2004.834804.

    Article  Google Scholar 

  18. Guo, K., & Labate, D. (2007). Optimally sparse multidimensional representation using shearlets. SIAM Journal on Mathematical Analysis, 39(1), 298–318. doi:10.1137/060649781.

    Article  MathSciNet  MATH  Google Scholar 

  19. Davenport, M. A., Duarte, M. F., Eldar, Y. C., & Kutyniok, G. (2012). Introduction to compressed sensing. In Y. C. Eldar, & G. Kutyniok (Eds.), Compressed sensing: Theory and applications. Cambridge, UK: Cambridge University Press.

  20. Pati, Y. C., Rezaiifar, R., & Krishnaprasad, P. S. (1993). Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In Proceedings of 27th Asilomar conference on signals, systems and computers 1–3 Nov 1993 (Vol. 1, pp. 40–44), doi:10.1109/ACSSC.1993.342465.

  21. Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998). atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33–61. doi:10.1137/s1064827596304010.

    Article  MathSciNet  Google Scholar 

  22. Starck, J. L., Elad, M., & Donoho, D. L. (2005). Image decomposition via the combination of sparse representations and a variational approach. IEEE Transactions on Image Processing, 14(10), 1570–1582. doi:10.1109/TIP.2005.852206.

    Article  MathSciNet  MATH  Google Scholar 

  23. Rubinstein, R., Bruckstein, A. M., & Elad, M. (2010). Dictionaries for sparse representation modeling. Proceedings of the IEEE, 98(6), 1045–1057. doi:10.1109/jproc.2010.2040551.

    Article  Google Scholar 

  24. Engan, K., Aase, S. O., & Hakon Husoy, J. (1999). Method of optimal directions for frame design. In IEEE international conference on acoustics, speech, and signal processing 1999 (Vol. 5, pp. 2443–2446). doi:10.1109/ICASSP.1999.760624.

  25. Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311–4322. doi:10.1109/tsp.2006.881199.

    Article  Google Scholar 

  26. Wald, L., Ranchin, T., & Mangolini, M. (1997). Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing, 63(6), 691–699.

    Google Scholar 

  27. Khan, M. M., Alparone, L., & Chanussot, J. (2009). Pansharpening quality assessment using the modulation transfer functions of instruments. IEEE Transactions on Geoscience and Remote Sensing, 47(11), 3880–3891. doi:10.1109/tgrs.2009.2029094.

    Article  Google Scholar 

  28. Alparone, L., Baronti, S., Garzelli, A., & Nencini, F. (2004). A global quality measurement of pan-sharpened multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 1(4), 313–317. doi:10.1109/lgrs.2004.836784.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the editor and anonymous reviewers for their detailed review and valuable comments. This paper is supported by scientific Research Fund of Hunan Provincial Education Department (No. 14B006).

Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, People’s Republic of China

    Altan-Ulzii Moonon & Shutao Li

  2. College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, 410114, People’s Republic of China

    Jianwen Hu

Authors
  1. Altan-Ulzii Moonon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianwen Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shutao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Altan-Ulzii Moonon.

Additional information

This article is part of the Topical Collection on Hyperspectral Imaging and Image Processing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moonon, AU., Hu, J. & Li, S. Remote Sensing Image Fusion Method Based on Nonsubsampled Shearlet Transform and Sparse Representation. Sens Imaging 16, 23 (2015). https://doi.org/10.1007/s11220-015-0125-0

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1007/s11220-015-0125-0

Keywords

Search

Navigation