Advertisement

Frontiers of Earth Science

, Volume 13, Issue 3, pp 656–667 | Cite as

A pan-sharpening method based on the ADMM algorithm

  • Yingxia Chen
  • Tingting Wang
  • Faming Fang
  • Guixu ZhangEmail author
Research Article
First Online:
  • 21 Downloads

Abstract

Pan-sharpening is a method of integrating low-resolution multispectral images with corresponding high-resolution panchromatic images to obtain multispectral images with high spectral and spatial resolution. A novel variational model for pan-sharpening is proposed in this paper. The model is mainly based on three hypotheses: 1) the pan-sharpened image can be linearly represented by the corresponding panchromatic image; 2) the low-resolution multispectral image is down-sampled from the highresolution multispectral image through the down-sampling operator; and 3) the satellite image has the low-rank property. Three energy components corresponding to these assumptions are integrated into a variational framework to obtain a total energy function. We adopt the alternating direction method of multipliers (ADMM) to optimize the total energy function. The experimental results show that the proposed method performs better than other mainstream methods in spectral and spatial information preserving aspect.

Keywords

pan-sharpening multispectral image panchromatic image variational framework energy function ADMM 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

We sincerely thank the editor and reviewers for their insightful comments and constructive suggestions on the article, and thank Dr. Thomas James Godfrey for helping us to revise the grammar. This work was supported in part by “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (Grant No. 17CG25), in part by the Key Project of the National Natural Science Foundation of China (Grant No. 61731009), and in part by the National Natural Science Foundation of China (Grant No. 61871185).

References

  1. Alparone L, Baronti S, Garzelli A, Nencini F (2004). A global quality measurement of pan-sharpened multispectral imagery. IEEE Geosci Remote Sens Lett, 1(4): 313–317CrossRefGoogle Scholar
  2. 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 Trans Geosci Remote Sens, 45(10): 3012–3021CrossRefGoogle Scholar
  3. Aiazzi B, Alparone L, Baronti S, Garzelli A (2002). Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Trans Geosci Remote Sens, 40(10): 2300–2312CrossRefGoogle Scholar
  4. Aly H, Sharma J (2014). A regularized model-based optimization framework for pan-sharpening. IEEE Trans Image Process, 23(6): 2596–2608CrossRefGoogle Scholar
  5. Ballester C, Caselles V, Igual L, Verdera J, Roug B (2006). Avariational model for P + XS image fusion. Int J Comput Vis, 69(1): 43–58CrossRefGoogle Scholar
  6. Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2010). Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn, 3(1): 1–122CrossRefGoogle Scholar
  7. Bredies K, Lorenz D A (2008). Soft-thresholding. J Fourier Anal Appl, 14(5–6): 813–837CrossRefGoogle Scholar
  8. Cai J, Candes E, Shen Z (2010). A singular value thresholding algorithm for matrix completion. SIAM J Optim, 20(4): 1956–1982CrossRefGoogle Scholar
  9. Candes E, Li X, Ma Y, Wright J (2011). Robust principal component analysis. J Assoc Comput Mach, 58(3): 1–37CrossRefGoogle Scholar
  10. Chen C, Li Y, Liu W, Huang J (2015). SIRF: simultaneous satellite image registration and fusion in a unified framework. IEEE Transactions on Image Processing, A Publication of the IEEE Signal Processing Society, 24(11): 4213CrossRefGoogle Scholar
  11. Chen C, Li Y, Liu W, Huang J (2014). Image fusion with local spectral consistency and dynamic gradient sparsity. IEEE Conference on Computer Vision & Pattern Recognition, 2760–2765Google Scholar
  12. Choi M (2006). A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter. IEEE Trans Geosci Remote Sens, 44(6): 1672–1682CrossRefGoogle Scholar
  13. Ding X, Jiang Y, Huang Y, Paisley J (2014). Pan-sharpening with a Bayesian nonparametric dictionary learning model. In: Proceeding of 17th International Conference of Artificial Intelligence and Statistics, 176–184Google Scholar
  14. Dong W, Shi G, Li X (2013). Nonlocal image restoration with bilateral variance estimation: a low-rank approach. IEEE Trans Image Process, 22(2): 700–711CrossRefGoogle Scholar
  15. Fang F, Li F, Shen C, Zhang G (2013). A variational approach for pansharpening. IEEE Trans Image Process, 22(7): 2822–2834CrossRefGoogle Scholar
  16. Goldstein T, O’Donoghue B, Setzer S, Baraniuk R (2014). Fast alternating direction optimization methods. SIAM J Imaging Sci, 7(3): 225–231CrossRefGoogle Scholar
  17. Laben C, Brower B (2000). Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. Websterny Uspenfieldny. USA: Eastman Kodak Company, 275–299Google Scholar
  18. Lu X, Zhang J (2014). Panchromatic and multispectral images fusion based on modified GS-SWT. Geoscience & Remote Sensing Symposium (IGARSS), 2530–2533Google Scholar
  19. Masi G, Cozzolino D, Verdoliva L, Scarpa G (2016). Pansharpening by convolutional neural networks. Remote Sens, 8(7): 594CrossRefGoogle Scholar
  20. Metwalli M, Nasr A, Faragallah O, Rabaie E E, Abbas A (2014). Efficient pan-sharpening of satellite images with the contourlet transform. Int J Remote Sens, 35(5): 1979–2002CrossRefGoogle Scholar
  21. Mezouar M E, Taleb N, Kpalma K, Ronsin J (2011). An IHS-based fusion for color distortion reduction and vegetation enhancement in IKONOS imagery. IEEE Trans Geosci Remote Sens, 49(5): 1590–1602CrossRefGoogle Scholar
  22. Moller M, Wittman T, Bertozzi A, Burger M (2013). A variational approach for sharpening high dimensional images. SIAM J Imaging Sci, 5(1): 150–178CrossRefGoogle Scholar
  23. Moller M, Wittman T, Bertozzi A (2008). Variational wavelet pansharpening. IEEE Trans Geosci Remote Sens, 1–9Google Scholar
  24. Park J, Kim K, Yang K (2001). Image fusion using multiresolution analysis. IEEE Int Geosci Remote Se, 2(2): 864–866Google Scholar
  25. Pushparaj J, Hegde A (2016). Evaluation of pan-sharpening methods for spatial and spectral quality. Appl Geomatics, 9(1): 1–12CrossRefGoogle Scholar
  26. Rahmani S, Strait M, Merkurjev D, Moeller M, Wittman T (2010). An adaptive HIS pan-sharpening method. IEEE Geosci Remote Sens Lett, 7(4): 746–750CrossRefGoogle Scholar
  27. Shah V, Younan N, King R (2008). An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Trans Geosci Remote Sens, 46(5): 1323–1335CrossRefGoogle Scholar
  28. Shah V, Younan N, King R (2007). An adaptive PCA-based approach to pan-sharpening. Remote Sens, 6748: 1–9Google Scholar
  29. Toet A, Hogervorst M (2003). Performance comparison of different gray level image fusion schemes through a universal image quality index. In: Kadar I (Ed.), Signal Processing, Sensor Fusion, and Target Recognition XII, SPIE-5096, 552–561CrossRefGoogle Scholar
  30. Vivone G, Alparone L, Chanussot J, Mura M, Garzelli A (2015). A critical comparison among pansharpening algorithms. IEEE Trans Geosci Remote Sens, 53(5): 2265–2586CrossRefGoogle Scholar
  31. Wang Q, Yu D, Shen Y (2009). An overview of image fusion metrics. IEEE Instrumentation & Measurement Technology Conference, Singapore, 918–923Google Scholar
  32. Wang S, Zhang Z (2012). Colorization by Matrix Completion. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI’12), AAAI Press, 1169–1175Google Scholar
  33. Wang Z, Bovik A (2002). A universal image quality index. IEEE Signal Process Lett, 9(3): 81–84CrossRefGoogle Scholar
  34. Wei Y, Yuan Q, Shen H, Zhang L (2017). Boosting the accuracy of multispectral image pansharpening by learning a deep residual network. IEEE Geosci Remote Sens Lett, 14(10): 1795–1799CrossRefGoogle Scholar
  35. Yang J, Fu X, Hu Y, Huang Y, Ding X, Paisley J (2017). PanNet: A deep network architecture for pan-sharpening. In Proceedings of IEEE International Conference on Computer Vision., 1753–1761Google Scholar
  36. Yocky D (1995). Image merging and data fusion by means of the discrete two-dimensional wavelet transform. J Opt Soc Am A Opt Image Sci Vis, 12(9): 1834–1841CrossRefGoogle Scholar
  37. Yuan Q, Wei Y, Meng X, Shen H, Zhang L (2018). A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3): 978–989CrossRefGoogle Scholar
  38. Zhang B (2010). Study on image fusion based on different fusion rules of wavelet transform. International Conference on Advanced Computer Theory & Engineering, 3(7): 649–653Google Scholar
  39. Zhang H, Roy D (2016). Computationally inexpensive Landsat 8 operational land imager (OLI) pansharpening. Remote Sens, 8(3): 180CrossRefGoogle Scholar
  40. Zhou J, Civco D, Silander J (1998). A wavelet transform method to merge Landsat TM and SPOT panchromatic data. Int J Remote Sens, 19(4): 743–757CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yingxia Chen
    • 1
    • 2
  • Tingting Wang
    • 1
  • Faming Fang
    • 1
  • Guixu Zhang
    • 1
    Email author
  1. 1.Department of Computer ScienceEast China Normal UniversityShanghaiChina
  2. 2.School of Computer ScienceYangtze UniversityJingzhouChina

Personalised recommendations

Cite article

Buy options