IEEE Geoscience and Remote Sensing Magazine - March 2021 - 80

[88]	 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.
[89]	 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.
[90]	 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, June 2016. doi: 10.1007/
s11220-016-0135-6.
[91]	 G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, " Pansharpening by convolutional neural networks, " Remote Sens., vol. 8,
no. 7, pp. 594:1-594:22, July 2016. doi: 10.3390/rs8070594.
[92]	 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.
[93]	 Y. Rao, L. He, and J. Zhu, " A residual convolutional neural network for pan-sharpening, " in Proc. Int. Workshop Remote Sensing with Intelligent Processing (RSIP), pp. 1-4, May 2017. doi:
10.1109/RSIP.2017.7958807.
[94]	 J. Yang, X. Fu, Y. Hu, Y. Huang, X. Ding, and J. Paisley, " PanNet:
A deep network architecture for pan-sharpening, " in Proc. 2017
IEEE Int. Conf. Computer Vision (ICCV), pp. 1753-1761. doi:
10.1109/ICCV.2017.193.
[95]	 G. Scarpa, S. Vitale, and D. Cozzolino, " Target-adaptive CNNbased pansharpening, " IEEE Trans. Geosci. Remote Sens., vol. 56, no.
9, pp. 5443-5457, Sept. 2018. doi: 10.1109/TGRS.2018.2817393.
[96]	 Q. Yuan, Y. Wei, X. Meng, H. Shen, and L. Zhang, " A multiscale and multidepth convolutional neural network for remote
sensing imagery pan-sharpening, " IEEE J. Sel. Topics Appl. Earth
Observ., vol. 11, no. 3, pp. 978-989, Mar. 2018. doi: 10.1109/
JSTARS.2018.2794888.
[97]	 Z. Shao and J. Cai, " Remote sensing image fusion with deep
convolutional neural network, " IEEE J. Sel. Topics Appl. Earth
Observ., vol. 11, no. 5, pp. 1656-1669, May 2018. doi: 10.1109/
JSTARS.2018.2805923.
[98]	 W. Yao, Z. Zeng, C. Lian, and H. Tang, " Pixel-wise regression using
U-Net and its application on pansharpening, " Neurocomputing, vol.
312, pp. 364-371, Oct. 2018. doi: 10.1016/j.neucom.2018.05.103.
[99]	 X. Liu, Y. Wang, and Q. Liu, " PSGAN: A generative adversarial
network for remote sensing image pan-sharpening, " in Proc.
2018 25th IEEE Int. Conf. Image Processing (ICIP), pp. 873-877.
doi: 10.1109/ICIP.2018.8451049.
[100]	Y. Zhang, C. Liu, M. Sun, and Y. Ou, " Pan-sharpening using
an efficient bidirectional pyramid network, " IEEE Trans. Geosci.
Remote Sens., vol. 57, no. 8, pp. 5549-5563, Aug. 2019. doi:
10.1109/TGRS.2019.2900419.
[101]	 S. Mei, X. Yuan, J. Ji, Y. Zhang, S. Wan, and Q. Du, " Hyperspectral image spatial super-resolution via 3D full convolutional neural network, " Remote Sens., vol. 9, no. 11, pp. 1139:1-
1139:22, Nov. 2017. doi: 10.3390/rs9111139.

80

[102]	C. Lanaras, J. Bioucas-Dias, S. Galliani, E. Baltsavias, and K.
Schindler, " Super-resolution of sentinel-2 images: Learning a
globally applicable deep neural network, " ISPRS J. Photogramm.
Remote Sens., vol. 146, pp. 305-319, Dec. 2018. doi: 10.1016/j.
isprsjprs.2018.09.018.
[103]	M. Gargiulo, A. Mazza, R. Gaetano, G. Ruello, and G. Scarpa,
" Fast super-resolution of 20 m sentinel-2 bands using convolutional neural networks, " Remote Sens., vol. 11, no. 22, pp.
2635:1-2635:18, Nov. 2019. doi: 10.3390/rs11222635.
[104]	Y. Qu, H. Qi, and C. Kwan, " Unsupervised sparse Dirichlet-Net
for hyperspectral image super-resolution, " in Proc. IEEE Conf.
Computer Vision Pattern Recognition, pp. 2511-2520, 2018.
[105]	Y. Qu, H. Qi, and C. Kwan, " Unsupervised and unregistered
hyperspectral image super-resolution with mutual DirichletNet, " 2019, arXiv 1904.12175.
[106]	C. Dong, C. C. Loy, K. He, and X. Tang, " Image super-resolution using deep convolutional networks, " IEEE Trans. Pattern
Anal. Mach. Intell., vol. 38, no. 2, pp. 295-307, Feb. 2016. doi:
10.1109/TPAMI.2015.2439281.
[107]	 K. He, X. Zhang, S. Ren, and J. Sun, " Deep residual learning for image recognition, " in Proc. 2016 IEEE Conf. Computer Vision Pattern
Recognition (CVPR), pp. 770-778, 2016. doi: 10.1109/CVPR.2016.90.
[108]	 S. Vitale and G. Scarpa, " A detail-preserving cross-scale learning
strategy for CNN-based pansharpening, " Remote Sens., vol. 12,
no. 3, pp. 348:1-348:20, Jan. 2020. doi: 10.3390/rs12030348.
[109]	J. Ma, W. Yu, C. Chen, P. Liang, X. Guo, and J. Jiang, " Pan-GAN:
An unsupervised pan-sharpening method for remote sensing
image fusion, " Inform. Fusion, vol. 62, pp. 110-120, Oct. 2020.
doi: 10.1016/j.inffus.2020.04.006.
[110]	 L. Wald, T. Ranchin, and M. Mangolini, " Fusion of satellite images of different spatial resolutions: Assessing the quality of
resulting images, " Photogramm. Eng. Remote Sens., vol. 63, no. 6,
pp. 691-699, June 1997.
[111]	 T. Ranchin, B. Aiazzi, L. Alparone, S. Baronti, and L. Wald, " Image fusion - the ARSIS concept and some successful implementation schemes, " ISPRS J. Photogramm. Remote Sens., vol. 58, nos.
1−2, pp. 4-18, June 2003. doi: 10.1016/S0924-2716(03)00013-3.
[112]	C. Thomas, T. Ranchin, L. Wald, and J. Chanussot, " Synthesis
of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics, " IEEE
Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1301-1312, May
2008. doi: 10.1109/TGRS.2007.912448.
[113]	F. Palsson, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, " Quantitative quality evaluation of pansharpened imagery: Consistency versus synthesis, " IEEE Trans. Geosci. Remote
Sens., vol. 54, no. 3, pp. 1247-1259, Mar. 2016. doi: 10.1109/
TGRS.2015.2476513.
[114]	 J. Zhou, D. L. Civco, and J. A. Silander, " A wavelet transform
method to merge landsat TM and SPOT panchromatic data, "
Int. J. Remote Sens., vol. 19, no. 4, pp. 743-757, May 1998. doi:
10.1080/014311698215973.
[115]	L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini,
and M. Selva, " Multispectral and panchromatic data fusion
assessment without reference, " Photogramm. Eng. Remote
Sens., vol. 74, no. 2, pp. 193-200, Feb. 2008. doi: 10.14358/
PERS.74.2.193.
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IEEE Geoscience and Remote Sensing Magazine - March 2021

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