Advertisement

Journal of the Indian Society of Remote Sensing

, Volume 46, Issue 12, pp 2069–2079 | Cite as

Quaternion-Based Sparse Model for Pan-Sharpening of IRS Satellite Images

  • D. Synthiya Vinothini
  • B. Sathya Bama
Research Article
  • 11 Downloads

Abstract

This paper considers the pan-sharpening problem of the IRS satellite images from the perspective of vector sparse representation model using quaternion matrix analysis. It selects the sparse basis in quaternion space, which uniformly transforms the color channels into an orthogonal color space. Moreover, the proposed quaternion model for pan-sharpening is more efficient than the conventional sparse model as the hyper-complex representation of color channels conserves the interrelationship among the chromatic channels. This paper also proposes a quaternion forward–backward pursuit algorithm that preserves the inherent chromatic structures in terms of spatial and spectral details during the vector reconstruction. The experimental result validates the efficacy of the proposed quaternion model and shows its potential as a powerful pan-sharpening tool for IRS data even for cloudy multispectral data.

Keywords

Image fusion Indian remote sensing satellite Multispectral image Panchromatic image Pan-sharpening Quaternions Remote sensing Sparse representation 

References

  1. Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., & Selva, M. (2003). An MTF-based spectral distortion minimizing model for pan-sharpening of very high resolution multispectral images of urban areas. In 2nd GRSS/ISPRS joint workshop on remote sensing and data fusion over urban areas, 2003 (pp. 90–94). IEEE.Google Scholar
  2. Aiazzi, B., Baronti, S., & Selva, M. (2007). Improving component substitution pansharpening through multivariate regression of MS + Pan data. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3230–3239.CrossRefGoogle Scholar
  3. Amolins, K., Zhang, Y., & Dare, P. (2007). Wavelet based image fusion techniques—An introduction, review and comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 62(4), 249–263.CrossRefGoogle Scholar
  4. Chavez, P., Sides, S. C., & Anderson, J. A. (1991). Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57(3), 295–303.Google Scholar
  5. Cheng, M., Wang, C., & Li, J. (2014). Sparse representation based pansharpening using trained dictionary. IEEE Geoscience and Remote Sensing Letters, 11(1), 293–297.CrossRefGoogle Scholar
  6. Choi, J., Yu, K., & Kim, Y. (2011). A new adaptive component-substitution-based satellite image fusion by using partial replacement. IEEE Transactions on Geoscience and Remote Sensing, 49(1), 295–309.CrossRefGoogle Scholar
  7. Garzelli, A., Nencini, F., Alparone, L., Aiazzi, B., & Baronti, S. (2004). Pan-sharpening of multispectral images: a critical review and comparison. In Geoscience and remote sensing symposium, 2004. IGARSS’04. Proceedings, 2004 IEEE International (Vol. 1). IEEE.Google Scholar
  8. Garzelli, A., Nencini, F., & Capobianco, L. (2008). Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(1), 228–236.CrossRefGoogle Scholar
  9. Gillespie, A. R., Kahle, A. B., & Walker, R. E. (1987). Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques. Remote Sensing of Environment, 22(3), 343–365.CrossRefGoogle Scholar
  10. Guo, M., Zhang, H., Li, J., Zhang, L., & Shen, H. (2014). An online coupled dictionary learning approach for remote sensing image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4), 1284–1294.CrossRefGoogle Scholar
  11. Jiang, C., Zhang, H., Shen, H., & Zhang, L. (2012). A practical compressed sensing-based pan-sharpening method. IEEE Geoscience and Remote Sensing Letters, 9(4), 629–633.CrossRefGoogle Scholar
  12. Jiang, C., Zhang, H., Shen, H., & Zhang, L. (2014). Two-step sparse coding for the pan-sharpening of remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5), 1792–1805.CrossRefGoogle Scholar
  13. Joshi, M. V., Bruzzone, L., & Chaudhuri, S. (2006). A model-based approach to multiresolution fusion in remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 44(9), 2549–2562.CrossRefGoogle Scholar
  14. Karahanoglu, N. B., & Erdogan, H. (2013). Compressed sensing signal recovery via forward–backward pursuit. Digital Signal Processing, 23(5), 1539–1548.CrossRefGoogle Scholar
  15. Laben, C. A., & Brower, B. V. (2000). U.S. Patent No. 6,011,875. Washington, DC: U.S. Patent and Trademark Office.Google Scholar
  16. 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.CrossRefGoogle Scholar
  17. Li, S., Yin, H., & Fang, L. (2013). Remote sensing image fusion via sparse representations over learned dictionaries. IEEE Transactions on Geoscience and Remote Sensing, 51(9), 4779–4789.CrossRefGoogle Scholar
  18. Li, Z., & Leung, H. (2009). Fusion of multispectral and panchromatic images using a restoration-based method. IEEE Transactions on Geoscience and Remote Sensing, 47(5), 1482–1491.CrossRefGoogle Scholar
  19. Ranchin, T., & Wald, L. (2000). Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation. Photogrammetric Engineering and Remote Sensing, 66(1), 49–61.Google Scholar
  20. Rao, C. V., Rao, J. M., Kumar, A. S., Jain, D. S., & Dadhwal, V. K. (2016). High spatial and spectral details retention fusion and evaluation. Journal of the Indian Society of Remote Sensing, 44(2), 167–175.CrossRefGoogle Scholar
  21. Tu, T. M., Su, S. C., Shyu, H. C., & Huang, P. S. (2001). A new look at IHS-like image fusion methods. Information Fusion, 2(3), 177–186.CrossRefGoogle Scholar
  22. Vicinanza, M. R., Restaino, R., Vivone, G., Dalla Mura, M., & Chanussot, J. (2015). A pansharpening method based on the sparse representation of injected details. IEEE Geoscience and Remote Sensing Letters, 12(1), 180–184.CrossRefGoogle Scholar
  23. Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G. A., et al. (2015). A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2565–2586.CrossRefGoogle Scholar
  24. Wald, L. (2002). Data fusion: Definitions and architectures: Fusion of images of different spatial resolutions. Paris: Presses des MINES.Google Scholar
  25. Wang, Z., & Bovik, A. C. (2002). A universal quality index. IEEE Signal Processing Letters, 20, 1–4.Google Scholar
  26. Xu, Y., Yu, L., Xu, H., Zhang, H., & Nguyen, T. (2015). Vector sparse representation of color image using quaternion matrix analysis. IEEE Transactions on Image Processing, 24(4), 1315–1329.CrossRefGoogle Scholar
  27. Yu, M., Xu, Y., & Sun, P. (2014, May). Single color image super-resolution using quaternion-based sparse representation. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5804–5808). IEEE.Google Scholar
  28. Yuhas, R. H., Goetz, A. F., & Boardman, J. W. (1992). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm.Google Scholar
  29. Zhang, L., Shen, H., Gong, W., & Zhang, H. (2012). Adjustable model-based fusion method for multispectral and panchromatic images. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(6), 1693–1704.CrossRefGoogle Scholar
  30. Zhu, X. X., & Bamler, R. (2013). A sparse image fusion algorithm with application to pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2827–2836.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations