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.
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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.
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.
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.
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.
Cheng, M., Wang, C., & Li, J. (2014). Sparse representation based pansharpening using trained dictionary. IEEE Geoscience and Remote Sensing Letters, 11(1), 293–297.
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.
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.
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.
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.
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.
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.
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.
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.
Karahanoglu, N. B., & Erdogan, H. (2013). Compressed sensing signal recovery via forward–backward pursuit. Digital Signal Processing, 23(5), 1539–1548.
Laben, C. A., & Brower, B. V. (2000). U.S. Patent No. 6,011,875. Washington, DC: U.S. Patent and Trademark Office.
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.
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.
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.
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.
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.
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.
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.
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.
Wald, L. (2002). Data fusion: Definitions and architectures: Fusion of images of different spatial resolutions. Paris: Presses des MINES.
Wang, Z., & Bovik, A. C. (2002). A universal quality index. IEEE Signal Processing Letters, 20, 1–4.
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.
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.
Yuhas, R. H., Goetz, A. F., & Boardman, J. W. (1992). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm.
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.
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.
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Synthiya Vinothini, D., Sathya Bama, B. Quaternion-Based Sparse Model for Pan-Sharpening of IRS Satellite Images. J Indian Soc Remote Sens 46, 2069–2079 (2018). https://doi.org/10.1007/s12524-018-0878-8
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DOI: https://doi.org/10.1007/s12524-018-0878-8