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Remote sensing image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a high-resolution image with limited spectral data and a low-resolution image rich in spectral information.
R. H. Yuhas, A. F. Goetz, and J. W. Boardman, “Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm,” in Proc. Summaries 3rd Annu. JPL Airborne Geosci. Workshop , vol. 1, 1992, pp. 147–149
1992
Earlier work this paper cites.
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, pp. 691–699, 1997
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, and M. Selva, “Mtf-tailored multiscale fusion of high-resolution ms and pan imagery,” Photogramm. Eng. Remote Sens. , vol. 72, no. 5, pp. 591–596, 2006
2006
Earlier work this paper cites.
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
2007
Earlier work this paper cites.
A. Garzelli and F. Nencini, “Hypercomplex quality assessment of multi/hyperspectral images,” IEEE Geosci. Remote Sens. Lett. , vol. 6, no. 4, pp. 662–665, 2009
2009
Earlier work this paper cites.
J. Choi, K. Yu, and Y. Kim, “A new adaptive component-substitution-based satellite image fusion by using partial replacement,” IEEE Trans. Geosci. Remote Sens. , 2010
2010
Earlier work this paper cites.
F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, “Generalized assorted pixel camera: Postcapture control of resolution, dynamic range, and spectrum,” IEEE Trans. Image Process. , vol. 19, no. 9, pp. 2241–2253, 2010
2010
Earlier work this paper cites.
N. Yokoya, T. Yairi, and A. Iwasaki, “Coupled non-negative matrix factorization (cnmf) for hyperspectral and multispectral data fusion: Application to pasture classification,” in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS) , 2011, pp. 1779–1782
2011
Earlier work this paper cites.
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, 2013
2013
Earlier work this paper cites.
G. Vivone, R. Restaino, M. Dalla Mura, G. Licciardi, and J. Chanussot, “Contrast and error-based fusion schemes for multispectral image pansharpening,” IEEE Geosci. Remote Sens. Lett. , vol. 11, no. 5, pp. 930–934, 2014
2014
Earlier work this paper cites.
L. Loncan, L. B. de Almeida, J. M. Bioucas-Dias, X. Briottet, J. Chanussot, N. Dobigeon, S. Fabre, W. Liao, G. A. Licciardi, M. Simões, J.-Y. Tourneret, M. A. Veganzones, G. Vivone, Q. Wei, and N. Yokoya, “Hyperspectral pansharpening: A review,” IEEE Geosci. Remote Sens. Mag. , vol. 3, no. 3, pp. 27–46, 2015
2015
Earlier work this paper cites.
G. Vivone, L. Alparone, J. Chanussot, M. Dalla Mura, A. Garzelli, G. A. Licciardi, R. Restaino, and L. Wald, “A critical comparison among pansharpening algorithms,” IEEE Trans. Geosci. Remote Sens. , vol. 53, no. 5, pp. 2565–2586, 2015
2015
Earlier work this paper cites.
M. Simões, J. Bioucas‐Dias, L. B. Almeida, and J. Chanussot, “A convex formulation for hyperspectral image superresolution via subspace-based regularization,” IEEE Trans. Geosci. Remote Sens. , vol. 53, no. 6, pp. 3373–3388, 2015
2015
Earlier work this paper cites.
M. Giuseppe, C. Davide, V. Luisa, and S. Giuseppe, “Pansharpening by convolutional neural networks,” Remote Sens. , vol. 8, no. 7, p. 594, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , June 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 30. Curran Associates, Inc., 2017
2017
Earlier work this paper cites.
J. Yang, X. Fu, Y. Hu, Y. Huang, X. Ding, and J. Paisley, “Pannet: A deep network architecture for pan-sharpening,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , 2017, pp. 1753–1761
2017
Earlier work this paper cites.
Y. Wei, Q. Yuan, X. Meng, H. Shen, L. Zhang, and M. Ng, “Multi-scale-and-depth convolutional neural network for remote sensed imagery pan-sharpening,” in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS) , 2017, pp. 3413–3416
2017
Earlier work this paper cites.
G. Vivone, R. Restaino, and J. Chanussot, “Full scale regression-based injection coefficients for panchromatic sharpening,” IEEE Trans. Image Process. , vol. 27, no. 7, pp. 3418–3431, 2018
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , June 2018, pp. 7132–7141
2018
Earlier work this paper cites.
G. Vivone, “Robust band-dependent spatial-detail approaches for panchromatic sharpening,” IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 9, pp. 6421–6433, 2019
2019
Earlier work this paper cites.
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, 2019
2019
Earlier work this paper cites.
L. He, J. Zhu, J. Li, A. Plaza, J. Chanussot, and B. Li, “Hyperpnn: Hyperspectral pansharpening via spectrally predictive convolutional neural networks,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. , vol. 12, no. 8, pp. 3092–3100, 2019
2019
Cited alongside, same era.
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, 2019
2019
Cited alongside, same era.
Y. Yang, W. Tu, S. Huang, and H. Lu, “Pcdrn: Progressive cascade deep residual network for pansharpening,” Remote Sens. , vol. 12, no. 4, 2020
2020
Cited alongside, same era.
J. Liu, Y. Feng, C. Zhou, and C. Zhang, “Pwnet: An adaptive weight network for the fusion of panchromatic and multispectral images,” Remote Sens. , vol. 12, no. 17, 2020
2020
Cited alongside, same era.
2022
Later among the works it cites.
Z.-R. Jin, T.-J. Zhang, T.-X. Jiang, G. Vivone, and L.-J. Deng, “Lagconv: Local-context adaptive convolution kernels with global harmonic bias for pansharpening,” Proc. AAAI Conf. Artif. Intell. (AAAI) , vol. 36, pp. 1113–1121, 2022
2022
Later among the works it cites.
Y. Liang, P. Zhang, Y. Mei, and T. Wang, “Pmacnet: Parallel multiscale attention constraint network for pan-sharpening,” IEEE Geosci. Remote Sens. Lett. , vol. 19, pp. 1–5, 2022
2022
Later among the works it cites.
Y.-W. Zhuo, T.-J. Zhang, J.-F. Hu, H.-X. Dou, T.-Z. Huang, and L.-J. Deng, “A deep-shallow fusion network with multidetail extractor and spectral attention for hyperspectral pansharpening,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. , vol. 15, pp. 7539–7555, 2022
2022
Later among the works it cites.
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L. He, J. Zhu, J. Li, D. Meng, J. Chanussot, and A. Plaza, “Spectral-fidelity convolutional neural networks for hyperspectral pansharpening,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.g , vol. 13, pp. 5898–5914, 2020
2020
Cited alongside, same era.
T. Xu, T.-Z. Huang, L.-J. Deng, X.-L. Zhao, and J. Huang, “Hyperspectral image superresolution using unidirectional total variation with tucker decomposition,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. , vol. 13, pp. 4381–4398, 2020
2020
Cited alongside, same era.
X. Liu, Q. Liu, and Y. Wang, “Remote sensing image fusion based on two-stream fusion network,” Inf. Fusion , vol. 55, pp. 1–15, 2020
2020
Cited alongside, same era.
G. Vivone, M. Dalla Mura, A. Garzelli, R. Restaino, G. Scarpa, M. O. Ulfarsson, L. Alparone, and J. Chanussot, “A new benchmark based on recent advances in multispectral pansharpening: Revisiting pansharpening with classical and emerging pansharpening methods,” IEEE Geosci. Remote Sens. Mag. , vol. 9, no. 1, pp. 53–81, 2021
2021
Cited alongside, same era.
J. Liu, C. Zhou, R. Fei, C. Zhang, and J. Zhang, “Pansharpening via neighbor embedding of spatial details,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. , vol. 14, pp. 4028–4042, 2021
2021
Cited alongside, same era.
X. Fu, W. Wang, Y. Huang, X. Ding, and J. Paisley, “Deep multiscale detail networks for multiband spectral image sharpening,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 32, no. 5, pp. 2090–2104, 2021
2021
Cited alongside, same era.
L.-J. Deng, G. Vivone, C. Jin, and J. Chanussot, “Detail injection-based deep convolutional neural networks for pansharpening,” IEEE Trans. Geosci. Remote Sens. , vol. 59, no. 8, pp. 6995–7010, 2021
2021
Cited alongside, same era.
S. Xu, J. Zhang, Z. Zhao, K. Sun, J. Liu, and C. Zhang, “Deep gradient projection networks for pan-sharpening,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , June 2021, pp. 1366–1375
2021
Cited alongside, same era.
J.-F. Hu, T.-Z. Huang, L.-J. Deng, H.-X. Dou, D. Hong, and G. Vivone, “Fusformer: A transformer-based fusion network for hyperspectral image super-resolution,” IEEE Geosci. Remote Sens. Lett. , vol. 19, pp. 1–5, 2022
2022
Later among the works it cites.
H. Lu, Y. Yang, S. Huang, X. Chen, B. Chi, A. Liu, and W. Tu, “Awfln: An adaptive weighted feature learning network for pansharpening,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–15, 2023
2023
Later among the works it cites.
R. Dian, A. Guo, and S. Li, “Zero-shot hyperspectral sharpening,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 10, pp. 12 650–12 666, 2023
2023
Later among the works it cites.
S. Peng, C. Guo, X. Wu, and L.-J. Deng, “U2net: A general framework with spatial-spectral-integrated double u-net for image fusion,” in Proc. ACM Int. Conf. Multimedia (ACM MM) . New York, NY, USA: Association for Computing Machinery, 2023, p. 3219–3227
2023
Later among the works it cites.
H. Gao, S. Li, J. Li, and R. Dian, “Multispectral image pan-sharpening guided by component substitution model,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–13, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Dong, Y. Yang, J. Qu, Y. Li, Y. Yang, and X. Jia, “Feature pyramid fusion network for hyperspectral pansharpening,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1–13, 2023
2023
Later among the works it cites.
Q. Ma, J. Jiang, X. Liu, and J. Ma, “Learning a 3d-cnn and transformer prior for hyperspectral image super-resolution,” Inf. Fusion , vol. 100, p. 101907, 2023
2023
Later among the works it cites.
S.-Q. Deng, L.-J. Deng, X. Wu, R. Ran, D. Hong, and G. Vivone, “Psrt: Pyramid shuffle-and-reshuffle transformer for multispectral and hyperspectral image fusion,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–15, 2023
2023
Later among the works it cites.
X. Wang, Q. Hu, Y. Cheng, and J. Ma, “Hyperspectral image super-resolution meets deep learning: A survey and perspective,” IEEE/CAA J. Autom. Sinica , vol. 10, no. 8, pp. 1668–1691, 2023
2023
Later among the works it cites.
2024
Closest in time.
H. Dai, Y. Yang, S. Huang, W. Wan, H. Lu, and X. Wang, “Pansharpening based on fuzzy logic and edge activity,” IEEE Geosci. Remote Sens. Lett. , vol. 21, pp. 1–5, 2024
2024
Closest in time.
Y. Jia, Q. Hu, R. Dian, J. Ma, and X. Guo, “Paps: Progressive attention-based pan-sharpening,” IEEE/CAA J. Autom. Sinica , vol. 11, no. 2, pp. 391–404, 2024
2024
Closest in time.
Y. Que, H. Xiong, X. Xia, J. You, and Y. Yang, “Integrating spectral and spatial bilateral pyramid networks for pansharpening,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. , vol. 17, pp. 3985–3998, 2024
2024
Closest in time.
Y. Duan, X. Wu, H. Deng, and L.-J. Deng, “Content-adaptive non-local convolution for remote sensing pansharpening,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , June 2024, pp. 27 738–27 747
2024
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2024
Closest in time.
C. Liu, K. Chen, B. Chen, H. Zhang, Z. Zou, and Z. Shi, “Rscama: Remote sensing image change captioning with state space model,” IEEE Geosci. Remote Sens. Lett. , vol. 21, pp. 1–5, 2024
2024
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K. Chen, B. Chen, C. Liu, W. Li, Z. Zou, and Z. Shi, “Rsmamba: Remote sensing image classification with state space model,” IEEE Geosci. Remote Sens. Lett. , vol. 21, pp. 1–5, 2024
2024
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2024
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W. Jiang, J. Zhang, D. Wang, Q. Zhang, Z. Wang, and B. Du, “LeMeViT: Efficient vision transformer with learnable meta tokens for remote sensing image interpretation,” in Proc. Int. Joint Conf. Artif. Intell. (IJCAI) , 2024
2024
Closest in time.