IEEE Geoscience and Remote Sensing Magazine - June 2022 - 322

Asilomar Conf. Signals, Syst. Comput., 2003, vol. 2, pp. 1398-
1402, doi: 10.1109/ACSSC.2003.1292216.
[44] Z. Tan, L. Di, M. Zhang, L. Guo, and M. Gao, " An enhanced
deep convolutional model for spatiotemporal image fusion, "
Remote Sens., vol. 11, no. 24, p. 2898, 2019, doi: 10.3390/
rs11242898.
[45] H. Zhang, Y. Song, C. Han, and L. Zhang, " Remote sensing
image spatiotemporal fusion using a generative adversarial
network, " IEEE Trans. Geosci. Remote Sens., vol. 59, no. 5, pp.
4273-4286, May 2021, doi: 10.1109/TGRS.2020.3010530.
[46] B. Wu, H. Duan, Z. Liu, and G. Sun, " SRPGAN: Perceptual generative
adversarial network for single image super resolution, "
2017, arXiv:1712.05927.
[47] C. Ledig et al., " Photo-realistic single image super-resolution
using a generative adversarial network, " in Proc. IEEE Conf.
Comput. Vis. Pattern Recognit., 2017, pp. 4681-4690.
[48] J. Johnson, A. Alahi, and L. Fei-Fei, " Perceptual losses for
real-time style transfer and super-resolution, " in Proc. Eur.
Conf. Comput. Vis., Springer-Verlag, 2016, pp. 694-711, doi:
10.1007/978-3-319-46475-6_43.
[49] X. Wang et al., " ESRGAN: Enhanced super-resolution generative
adversarial networks, " in Proc. Eur. Conf. Comput. Vis. Workshops
(ECCVW), Sep. 2018, pp. 63-79.
[50] H. Shen, X. Meng, and L. Zhang, " An integrated framework
for the spatio-temporal-spectral fusion of remote sensing
images, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp.
7135-7148, 2016, doi: 10.1109/TGRS.2016.2596290.
[51] L. Zhang and H. Shen, " Progress and future of remote sensing
data fusion, " Yaogan Xuebao/J. Remote Sens., vol. 20, no. 5, pp.
1050-1061, 2016.
[52] H. Zhang and J. Ma, " GTP-PNET: A residual learning network
based on gradient transformation prior for pansharpening, " ISPRS
J. Photogrammetry Remote Sens., vol. 172, pp. 223-239, Feb.
2021, doi: 10.1016/j.isprsjprs.2020.12.014.
[53] M. Jiang, H. Shen, J. Li, Q. Yuan, and L. Zhang, " A differential
information residual convolutional neural network
for pansharpening, " ISPRS J. Photogrammetry Remote Sens.,
vol. 163, pp. 257-271, May 2020, doi: 10.1016/j.isprsjprs.
2020.03.006.
[54] Q. Liu, H. Zhou, Q. Xu, X. Liu, and Y. Wang, " PSGAN: A
generative adversarial network for remote sensing image
pan-sharpening, " IEEE Trans. Geosci. Remote Sens., vol. 59,
no. 12, pp. 10,227-10,242, 2020, doi: 10.1109/TGRS.2020.
3042974.
[55] W. Xie, Y. Cui, Y. Li, J. Lei, Q. Du, and J. Li, " HPGAN: Hyperspectral
pansharpening using 3-D generative adversarial
networks, " IEEE Trans. Geosci. Remote Sens., vol. 59, no. 1, pp.
463-477, 2021, doi: 10.1109/TGRS.2020.2994238.
[56] J. Ma, W. Yu, C. Chen, P. Liang, X. Guo, and J. Jiang, " PANGAN:
An unsupervised pan-sharpening method for remote
sensing image fusion, " Inf. Fusion, vol. 62, pp. 110-120, Oct.
2020, doi: 10.1016/j.inffus.2020.04.006.
[57] F. Ozcelik, U. Alganci, E. Sertel, and G. Unal, " Rethinking
CNN-based pansharpening: Guided colorization of panchromatic
images via GANs, " IEEE Trans. Geosci. Remote Sens., vol.
59, no. 4, pp. 1-16, 2020, doi: 10.1109/TGRS.2020.3010441.
322
[58] C. Zhou, J. Zhang, J. Liu, C. Zhang, R. Fei, and S. Xu, " PercepPan:
Towards unsupervised pan-sharpening based on perceptual
loss, " Remote Sens., vol. 12, no. 14, p. 2318, 2020. [Online].
Available: https://www.mdpi.com/2072-4292/12/14/2318, doi:
10.3390/rs12142318.
[59] Z. Shao, Z. Lu, M. Ran, L. Fang, J. Zhou, and Y. Zhang, " Residual
encoder-decoder conditional generative adversarial network
for pansharpening, " IEEE Geosci. Remote Sens. Lett., vol. 17, no.
9, pp. 1573-1577, 2020, doi: 10.1109/LGRS.2019.2949745.
[60] J. Chen, Y. Pan, and Y. Chen, " Remote sensing image fusion
based on Bayesian GAN, " 2020. [Online]. Available: http://
arxiv.org/abs/2009.09465
[61] L. Wald, T. Ranchin, and M. Mangolini, " Fusion of satellite
images of different spatial resolution: Assessing the quality of
resulting images, " Photogrammetry Eng. Remote Sens., vol. 63, no.
6, pp. 691-699, 1997.
[62] Y. Kim, C. Lee, D. Han, Y. Kim, and Y. Kim, " Improved
additive-wavelet image fusion, " IEEE Geosci. Remote Sens.
Lett., vol. 8, no. 2, pp. 263-267, 2010, doi: 10.1109/LGRS.
2010.2067192.
[63] B. Aiazzi, S. Baronti, and M. Selva, " Improving component
substitution pansharpening through multivariate regression
of MS + Pan data, " IEEE Trans. Geosci. Remote Sens., vol. 45, no.
10, pp. 3230-3239, 2007, doi: 10.1109/TGRS.2007.901007.
[64] X. Otazu, M. González-Audícana, O. Fors, and J. Núñez,
" Introduction of sensor spectral response into image fusion
methods. application to wavelet-based methods, " IEEE Trans.
Geosci. Remote Sens., vol. 43, no. 10, pp. 2376-2385, 2005, doi:
10.1109/TGRS.2005.856106.
[65] 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, 2017, doi: 10.1109/LGRS.2017.2736020.
[66] J. Yang, X. Fu, Y. Hu, Y. Huang, X. Ding, and J. Paisley, " Pannet:
A deep network architecture for pan-sharpening, " in Proc. IEEE
Int. Conf. Comput. Vis., 2017, pp. 5449-5457.
[67] F. Dadrass Javan, F. Samadzadegan, S. Mehravar, A. Toosi, R.
Khatami, and A. Stein, " A review of image fusion techniques
for pan-sharpening of high-resolution satellite imagery, " ISPRS
J. Photogramm. Remote Sens., vol. 171, pp. 101-117, Jan. 2021,
doi: 10.1016/j.isprsjprs.2020.11.001.
[68] N. Yokoya, C. Grohnfeldt, and J. Chanussot, " Hyperspectral
and multispectral data fusion: A comparative
review of the recent literature, " IEEE Geosci. Remote Sens.
Mag., vol. 5, no. 2, pp. 29-56, 2017, doi: 10.1109/MGRS.
2016.2637824.
[69] R. Dian, S. Li, B. Sun, and A. Guo, " Recent advances and new
guidelines on hyperspectral and multispectral
image fusion, "
Inf. Fusion, vol. 69, pp. 40-51, May 2021. doi: 10.1016/j.
inffus.2020.11.001.
[70] M. Imani and H. Ghassemian, " An overview on spectral and
spatial information fusion for hyperspectral image classification:
Current trends and challenges, " Inf. Fusion, vol. 59, pp.
59-83, Jul. 2020, doi: 10.1016/j.inffus.2020.01.007.
[71] Y. Blau, R. Mechrez, R. Timofte, T. Michaeli, and L. Zelnik-Manor,
" The 2018 pirm challenge on perceptual image super-resolution, "
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2022
https://www.mdpi.com/2072-4292/12/14/2318 http://www.arxiv.org/abs/2009.09465 http://www.arxiv.org/abs/2009.09465
IEEE Geoscience and Remote Sensing Magazine - June 2022

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