IEEE Geoscience and Remote Sensing Magazine - September 2022 - 313

Inf. Fusion, vol. 69, pp. 40-51, May 2021, doi: 10.1016/j.
inffus.2020.11.001.
[46] R. Dian and S. Li, " Hyperspectral image super-resolution via
subspace-based low tensor multi-rank regularization, " IEEE
Trans. Image Process., vol. 28, no. 10, pp. 5135-5146, Oct. 2019,
doi: 10.1109/TIP.2019.2916734.
[47] Z.-C. Wu et al., " A new variational approach based on proximal
deep injection and gradient intensity similarity for spatiospectral
image fusion, " IEEE J. Sel. Topics Appl. Earth Observ.
Remote Sens., vol. 13, pp. 6277-6290, Oct. 2020, doi: 10.1109/
JSTARS.2020.3030129.
[48] J. Duran, A. Buades, B. Coll, C. Sbert, and G. Blanchet, " A survey
of pansharpening methods with a new band-decoupled
variational model, " ISPRS J. Photogrammetry Remote Sens.,
vol. 125, pp. 78-105, Mar. 2017, doi: 10.1016/j.isprsjprs.2016.
12.013.
[49] C. Ballester, V. Caselles, L. Igual, J. Verdera, and B. Rougé, " A
variational model for P+XS image fusion, " Int. J. Comput. Vis.,
vol. 69, no. 1, pp. 43-58, Aug. 2006, doi: 10.1007/s11263-006
-6852-x.
[50] F. Palsson, J. R. Sveinsson, and M. O. Ulfarsson, " A new pansharpening
algorithm based on total variation, " IEEE Geosci.
Remote Sens. Lett., vol. 11, no. 1, pp. 318-322, Jan. 2014, doi:
10.1109/LGRS.2013.2257669.
[51] X. He, L. Condat, J. Bioucas-Dias, J. Chanussot, and J. Xia, " A
new pansharpening method based on spatial and spectral
sparsity priors, " IEEE Trans. Image Process., vol. 23, no. 9, pp.
4160-4174, Sep. 2014, doi: 10.1109/TIP.2014.2333661.
[52] H. A. Aly and G. Sharma, " A regularized model-based optimization
framework for pan-sharpening, " IEEE Trans. Image Process.,
vol. 23, no. 6, pp. 2596-2608, Apr. 2014, doi: 10.1109/
TIP.2014.2316641.
[53] M. Möller, T. Wittman, A. L. Bertozzi, and M. Burger, " A variational
approach for sharpening high dimensional images, "
SIAM J. Imag. Sci., vol. 5, no. 1, pp. 150-178, Jan. 2012, doi:
10.1137/100810356.
[54] G. Zhang, F. Fang, A. Zhou, and F. Li, " Pan-sharpening of
multi-spectral images using a new variational model, " Int. J.
Remote Sens., vol. 36, no. 5, pp. 1484-1508, Mar. 2015, doi:
10.1080/01431161.2015.1014973.
[55] F. Palsson, M. O. Ulfarsson, and J. R. Sveinsson, " Modelbased
reduced-rank pansharpening, " IEEE Geosci. Remote Sens.
Lett., vol. 17, no. 4, pp. 656-660, Apr. 2020, doi: 10.1109/
LGRS.2019.2926681.
[56] D. Fasbender, J. Radoux, and P. Bogaert, " Bayesian data fusion
for adaptable image pansharpening, " IEEE Trans. Geosci. Remote
Sens., vol. 46, no. 6, pp. 1847-1857, Jun. 2008, doi: 10.1109/
TGRS.2008.917131.
[57] Y. Zhang, S. De Backer, and P. Scheunders, " Noise-resistant
wavelet-based Bayesian fusion of multispectral and hyperspectral
images, " IEEE Trans. Geosci. Remote Sens., vol. 47,
no. 11, pp. 3834-3843, Nov. 2009, doi: 10.1109/TGRS.2009.
2017737.
[58] F. Palsson, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson,
" Model-based fusion of multi- and hyperspectral
images using PCA and wavelets, " IEEE Trans. Geosci. Remote
SEPTEMBER 2022 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
Sens., vol. 53, no. 5, pp. 2652-2663, May 2015, doi: 10.1109/
TGRS.2014.2363477.
[59] Y. Zhang, A. Duijster, and P. Scheunders, " A Bayesian restoration
approach for hyperspectral images, " IEEE Trans. Geosci.
Remote Sens., vol. 50, no. 9, pp. 3453-3462, Sep. 2012, doi:
10.1109/TGRS.2012.2184122.
[60] S. Li and B. Yang, " A new pan-sharpening method using a
compressed sensing technique, " IEEE Trans. Geosci. Remote
Sens., vol. 49, no. 2, pp. 738-746, Feb. 2011, doi: 10.1109/
TGRS.2010.2067219.
[61] C. Jiang, H. Zhang, H. Shen, and L. Zhang, " A practical compressed
sensing-based pan-sharpening method, " IEEE Geosci.
Remote Sens. Lett., vol. 9, no. 4, pp. 629-633, Jul. 2015, doi:
10.1109/LGRS.2011.2177063.
[62] S. Li, H. Yin, and L. Fang, " Remote sensing image fusion via
sparse representations over learned dictionaries, " IEEE Trans.
Geosci. Remote Sens., vol. 51, no. 9, pp. 4779-4789, Sep. 2013,
doi: 10.1109/TGRS.2012.2230332.
[63] M. Cheng, C. Wang, and J. Li, " Sparse representation based
pansharpening using trained dictionary, " IEEE Geosci. Remote
Sens. Lett., vol. 11, no. 1, pp. 293-297, Jan. 2014, doi: 10.1109/
LGRS.2013.2256875.
[64] X. X. Zhu and R. Bamler, " A sparse image fusion algorithm
with application to pan-sharpening, " IEEE Trans. Geosci. Remote
Sens., vol. 51, no. 5, pp. 2827-2836, May 2013, doi: 10.1109/
TGRS.2012.2213604.
[65] X. X. Zhu, C. Grohnfeld, and R. Bamler, " Exploiting joint sparsity
for pansharpening: The J-sparseFI algorithm, " IEEE Trans.
Geosci. Remote Sens., vol. 54, no. 5, pp. 2664-2681, May 2016,
doi: 10.1109/TGRS.2015.2504261.
[66] M. R. Vicinanza, R. Restaino, G. Vivone, M. Dalla Mura, G.
Licciardi, and J. Chanussot, " A pansharpening method based
on the sparse representation of injected details, " IEEE Geosci.
Remote Sens. Lett., vol. 12, no. 1, pp. 180-184, Jan. 2015, doi:
10.1109/LGRS.2014.2331291.
[67] X. Tian, Y. Chen, C. Yang, X. Gao, and J. Ma, " A variational pansharpening
method based on gradient sparse representation, "
IEEE Signal Process. Lett., vol. 27, pp. 1180-1184, Jul. 2020, doi:
10.1109/LSP.2020.3007325.
[68] W. Huang, L. Xiao, Z. Wei, H. Liu, and S. Tang, " A new pansharpening
method with deep neural networks, " IEEE Geosci.
Remote Sens. Lett., vol. 12, no. 5, pp. 1037-1041, May 2015, doi:
10.1109/LGRS.2014.2376034.
[69] G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, " Pansharpening
by convolutional neural networks, " Remote
Sens., vol. 8, no. 7, p. 594, Jul. 2016, doi: 10.3390/
rs8070594.
[70] J. Zhong, B. Yang, G. Huang, F. Zhong, and Z. Chen, " Remote
sensing image fusion with convolutional neural network, " Sens.
Imag., vol. 17, no. 1, pp. 1-16, Jun. 2016, doi: 10.1007/s11220
-016-0135-6.
[71] Y. Wei, Q. Yuan, H. Shen, and L. Zhang, " Boosting the accuracy
of multispectral image pansharpening by learning a deep
residual network, " IEEE Geosci. Remote Sens. Lett., vol. 14,
no. 10, pp. 1795-1799, Oct. 2017, doi: 10.1109/LGRS.2017.
2736020.
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