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Volume 10 Issue 8
Aug.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(8): 1668-1691
X. Y. Wang, Q. Hu, Y. S. Cheng, and  J. Y. Ma,  “Hyperspectral image super-resolution meets deep learning: A survey and perspective,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1668–1691, Aug. 2023. doi: 10.1109/JAS.2023.123681
Citation: X. Y. Wang, Q. Hu, Y. S. Cheng, and  J. Y. Ma,  “Hyperspectral image super-resolution meets deep learning: A survey and perspective,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1668–1691, Aug. 2023. doi: 10.1109/JAS.2023.123681
shu

Hyperspectral Image Super-Resolution Meets Deep Learning: A Survey and Perspective

doi: 10.1109/JAS.2023.123681
Funds:  This work was supported in part by the National Natural Science Foundation of China (62276192)
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    Abstract

  • Hyperspectral image super-resolution, which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation, aims to improve the spatial resolution of the hyperspectral image, which is beneficial for subsequent applications. The development of deep learning has promoted significant progress in hyperspectral image super-resolution, and the powerful expression capabilities of deep neural networks make the predicted results more reliable. Recently, several latest deep learning technologies have made the hyperspectral image super-resolution method explode. However, a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent. To this end, in this survey, we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary information. Then, we review the learning-based methods in three categories, including single hyperspectral image super-resolution, panchromatic-based hyperspectral image super-resolution, and multispectral-based hyperspectral image super-resolution. Subsequently, we summarize the commonly used hyperspectral dataset, and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively. Moreover, we briefly introduce several typical applications of hyperspectral image super-resolution, including ground object classification, urban change detection, and ecosystem monitoring. Finally, we provide the conclusion and challenges in existing learning-based methods, looking forward to potential future research directions.

     

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    • 21 https://citius.usc.es/investigacion/datasets/hyperspectral-change-detection-dataset..2 https://eros.usgs.gov/doi-remote-sensing-activities/2015/usgs/hyperspectral-analysis-submerged-aquatic-vegetation.
    1https://eros.usgs.gov/doi-remote-sensing-activities/2015/usgs/hyperspectral-analysis-submerged-aquatic-vegetation.
    References(205)
  • [1]
    N. Yokoya, C. Grohnfeldt, and J. Chanussot, “Hyperspectral and multispectral data fusion: A comparative review of the recent literature,” IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 2, pp. 29–56, 2017. doi: 10.1109/MGRS.2016.2637824
    [2]
    I. Mariotto, P. S. Thenkabail, A. Huete, E. T. Slonecker, and A. Platonov, “Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the hyspiri mission,” Remote Sensing Environment, vol. 139, pp. 291–305, 2013. doi: 10.1016/j.rse.2013.08.002
    [3]
    M. Marshall and P. Thenkabail, “Advantage of hyperspectral eo-1 hyperion over multispectral ikonos, geoeye-1, worldview-2, landsat etm+, and modis vegetation indices in crop biomass estimation,” ISPRS J. Photogrammetry Remote Sensing, vol. 108, pp. 205–218, 2015. doi: 10.1016/j.isprsjprs.2015.08.001
    [4]
    Y. Li, W. Xie, and H. Li, “Hyperspectral image reconstruction by deep convolutional neural network for classification,” Pattern Recognition, vol. 63, pp. 371–383, 2017. doi: 10.1016/j.patcog.2016.10.019
    [5]
    G. A. Carter, K. L. Lucas, G. A. Blossom, C. L. Lassitter, D. M. Holiday, D. S. Mooneyhan, D. R. Fastring, T. R. Holcombe, and J. A. Griffith, “Remote sensing and mapping of tamarisk along the colorado river, USA: A comparative use of summer-acquired hyperion, thematic mapper and quickbird data,” Remote Sensing, vol. 1, no. 3, pp. 318–329, 2009. doi: 10.3390/rs1030318
    [6]
    S. Liu, D. Marinelli, L. Bruzzone, and F. Bovolo, “A review of change detection in multitemporal hyperspectral images: Current techniques, applications, and challenges,” IEEE Geoscience Remote Sensing Magazine, vol. 7, no. 2, pp. 140–158, 2019. doi: 10.1109/MGRS.2019.2898520
    [7]
    N. Akhtar, F. Shafait, and A. Mian, “Bayesian sparse representation for hyperspectral image super resolution,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2015, pp. 3631–3640.
    [8]
    M. Simoes, J. Bioucas-Dias, L. B. Almeida, and J. Chanussot, “A convex formulation for hyperspectral image superresolution via subspace-based regularization,” IEEE Trans. Geoscience Remote Sensing, vol. 53, no. 6, pp. 3373–3388, 2014.
    [9]
    Y. Zhang, S. De Backer, and P. Scheunders, “Noise-resistant wavelet-based bayesian fusion of multispectral and hyperspectral images,” IEEE Trans. Geoscience Remote Sensing, vol. 47, no. 11, pp. 3834–3843, 2009. doi: 10.1109/TGRS.2009.2017737
    [10]
    R. Dian, L. Fang, and S. Li, “Hyperspectral image super-resolution via non-local sparse tensor factorization,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2017, pp. 5344–5353.
    [11]
    S. Li, R. Dian, L. Fang, and J. M. Bioucas-Dias, “Fusing hyperspectral and multispectral images via coupled sparse tensor factorization,” IEEE Trans. Image Processing, vol. 27, no. 8, pp. 4118–4130, 2018. doi: 10.1109/TIP.2018.2836307
    [12]
    Y. Xu, Z. Wu, J. Chanussot, and Z. Wei, “Nonlocal patch tensor sparse representation for hyperspectral image super-resolution,” IEEE Trans. Image Processing, vol. 28, no. 6, pp. 3034–3047, 2019. doi: 10.1109/TIP.2019.2893530
    [13]
    R. Dian, S. Li, and L. Fang, “Learning a low tensor-train rank representation for hyperspectral image super-resolution,” IEEE Trans. Neural Networks Learning Systems, vol. 30, no. 9, pp. 2672–2683, 2019. doi: 10.1109/TNNLS.2018.2885616
    [14]
    R. Dian, S. Li, L. Fang, T. Lu, and J. M. Bioucas-Dias, “Nonlocal sparse tensor factorization for semiblind hyperspectral and multispectral image fusion,” IEEE Trans. Cybernetics, vol. 50, no. 10, pp. 4469–4480, 2020. doi: 10.1109/TCYB.2019.2951572
    [15]
    R. Kawakami, Y. Matsushita, J. Wright, M. Ben-Ezra, Y.-W. Tai, and K. Ikeuchi, “High-resolution hyperspectral imaging via matrix factorization,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2011, pp. 2329–2336.
    [16]
    N. Yokoya, T. Yairi, and A. Iwasaki, “Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 50, no. 2, pp. 528–537, 2012. doi: 10.1109/TGRS.2011.2161320
    [17]
    C. Lanaras, E. Baltsavias, and K. Schindler, “Hyperspectral super-resolution by coupled spectral unmixing,” in Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 3586–3594.
    [18]
    W. Dong, F. Fu, G. Shi, X. Cao, J. Wu, G. Li, and X. Li, “Hyperspectral image super-resolution via non-negative structured sparse representation,” IEEE Trans. Image Processing, vol. 25, no. 5, pp. 2337–2352, 2016. doi: 10.1109/TIP.2016.2542360
    [19]
    R. Dian and S. Li, “Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization,” IEEE Trans. Image Processing, vol. 28, no. 10, pp. 5135–5146, 2019. doi: 10.1109/TIP.2019.2916734
    [20]
    R. Loizzo, R. Guarini, F. Longo, T. Scopa, R. Formaro, C. Facchinetti, and G. Varacalli, “Prisma: The italian hyperspectral mission,” in Proc. IEEE Int. Geoscience Remote Sensing Symp., 2018, pp. 175–178.
    [21]
    Y.-N. Liu, D.-X. Sun, X.-N. Hu, X. Ye, Y.-D. Li, S.-F. Liu, K.-Q. Cao, M.-Y. Chai, J. Zhang, Y. Zhang, et al., “The advanced hyperspectral imager: Aboard China’s GaoFen-5 satellite,” IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 4, pp. 23–32, 2019. doi: 10.1109/MGRS.2019.2927687
    [22]
    L. Loncan, L. B. De Almeida, J. M. Bioucas-Dias, X. Briottet, J. Chanussot, N. Dobigeon, S. Fabre, W. Liao, G. A. Licciardi, M. Simoes, et al., “Hyperspectral pansharpening: A review,” IEEE Geoscience Remote Sensing Magazine, vol. 3, no. 3, pp. 27–46, 2015. doi: 10.1109/MGRS.2015.2440094
    [23]
    H. Zhang, H. Xu, X. Tian, J. Jiang, and J. Ma, “Image fusion meets deep learning: A survey and perspective,” Information Fusion, vol. 76, pp. 323–336, 2021. doi: 10.1016/j.inffus.2021.06.008
    [24]
    R. Dian, S. Li, B. Sun, and A. Guo, “Recent advances and new guidelines on hyperspectral and multispectral image fusion,” Information Fusion, vol. 69, pp. 40–51, 2021. doi: 10.1016/j.inffus.2020.11.001
    [25]
    D. Sara, A. K. Mandava, A. Kumar, S. Duela, and A. Jude, “Hyperspectral and multispectral image fusion techniques for high resolution applications: A review,” Earth Science Informatics, vol. 14, no. 4, pp. 1685–1705, 2021. doi: 10.1007/s12145-021-00621-6
    [26]
    G. Vivone, “Multispectral and hyperspectral image fusion in remote sensing: A survey,” Information Fusion, vol. 89, pp. 405–417, 2023. doi: 10.1016/j.inffus.2022.08.032
    [27]
    Q. Ma, J. Jiang, X. Liu, and J. Ma, “Multi-task interaction learning for spatiospectral image super-resolution,” IEEE Trans. Image Processing, vol. 31, pp. 2950–2961, 2022. doi: 10.1109/TIP.2022.3161834
    [28]
    Q. Ma, J. Jiang, X. Liu, and J. Ma, “Deep unfolding network for spatiospectral image super-resolution,” IEEE Trans. Computational Imaging, vol. 8, pp. 28–40, 2022. doi: 10.1109/TCI.2021.3136759
    [29]
    Y. Cai, J. Lin, X. Hu, H. Wang, X. Yuan, Y. Zhang, R. Timofte, and L. Van Gool, “Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2022, pp. 17502–17511.
    [30]
    X. Hu, Y. Cai, J. Lin, H. Wang, X. Yuan, Y. Zhang, R. Timofte, and L. Van Gool, “HDNet: High-resolution dual-domain learning for spectral compressive imaging,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2022, pp. 17542–17551.
    [31]
    R. Dian, T. Shan, W. He, and H. Liu, “Spectral super-resolution via model-guided cross-fusion network,” IEEE Trans. Neural Networks and Learning Systems, 2023. DOI: 10.1109/TNNLS.2023.3238506
    [32]
    X. Fu, W. Wang, Y. Huang, X. Ding, and J. Paisley, “Deep multiscale detail networks for multiband spectral image sharpening,” IEEE Trans. Neural Networks Learning Systems, vol. 32, no. 5, pp. 2090–2104, 2021. doi: 10.1109/TNNLS.2020.2996498
    [33]
    X. Meng, X. Meng, Q. Liu, J. Shu, F. Shao, G. Yang, and W. Sun, “Integrated fusion for panchromatic, multispectral, hyperspectral remote sensing images with different swath widths,” IEEE Geoscience Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
    [34]
    X. Tian, W. Zhang, Y. Chen, Z. Wang, and J. Ma, “Hyperfusion: A computational approach for hyperspectral, multispectral, and panchromatic image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–16, 2022.
    [35]
    X. Tian, W. Zhang, D. Yu, and J. Ma, “Sparse tensor prior for hyperspectral, multispectral, and panchromatic image fusion,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 284–286, 2023. doi: 10.1109/JAS.2022.106013
    [36]
    K. Li, W. Zhang, D. Yu, and X. Tian, “HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion,” ISPRS J. Photogrammetry Remote Sensing, vol. 188, pp. 30–44, 2022. doi: 10.1016/j.isprsjprs.2022.04.001
    [37]
    Y. Li, J. Hu, X. Zhao, W. Xie, and J. Li, “Hyperspectral image super-resolution using deep convolutional neural network,” Neurocomputing, vol. 266, pp. 29–41, 2017. doi: 10.1016/j.neucom.2017.05.024
    [38]
    J. Hu, Y. Li, and W. Xie, “Hyperspectral image super-resolution by spectral difference learning and spatial error correction,” IEEE Geoscience Remote Sensing Letters, vol. 14, no. 10, pp. 1825–1829, 2017. doi: 10.1109/LGRS.2017.2737637
    [39]
    Z. He and L. Liu, “Hyperspectral image super-resolution inspired by deep laplacian pyramid network,” Remote Sensing, vol. 10, no. 12, p. 1939, 2018. doi: 10.3390/rs10121939
    [40]
    J. Yang, Y.-Q. Zhao, J. C.-W. Chan, and L. Xiao, “A multi-scale wavelet 3D-CNN for hyperspectral image super-resolution,” Remote Sensing, vol. 11, no. 13, p. 1557, 2019. doi: 10.3390/rs11131557
    [41]
    Y. Yuan, X. Zheng, and X. Lu, “Hyperspectral image superresolution by transfer learning,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 10, no. 5, pp. 1963–1974, 2017. doi: 10.1109/JSTARS.2017.2655112
    [42]
    W. Xie, X. Jia, Y. Li, and J. Lei, “Hyperspectral image super-resolution using deep feature matrix factorization,” IEEE Trans. Geoscience Remote Sensing, vol. 57, no. 8, pp. 6055–6067, 2019. doi: 10.1109/TGRS.2019.2904108
    [43]
    J. Hu, M. Zhao, and Y. Li, “Hyperspectral image super-resolution by deep spatial-spectral exploitation,” Remote Sensing, vol. 11, no. 10, p. 1229, 2019. doi: 10.3390/rs11101229
    [44]
    C. Zou and X. Huang, “Hyperspectral image super-resolution combining with deep learning and spectral unmixing,” Signal Processing: Image Communication, vol. 84, p. 115833, 2020. doi: 10.1016/j.image.2020.115833
    [45]
    X. Lu, D. Yang, J. Zhang, and F. Jia, “Hyperspectral image super-resolution based on spatial correlation-regularized unmixing convolutional neural network,” Remote Sensing, vol. 13, no. 20, p. 4074, 2021. doi: 10.3390/rs13204074
    [46]
    Y. Li, L. Zhang, C. Dingl, W. Wei, and Y. Zhang, “Single hyperspectral image super-resolution with grouped deep recursive residual network,” in Proc. IEEE Int. Conf. Multimedia Big Data, 2018, pp. 1–4.
    [47]
    J. Jiang, H. Sun, X. Liu, and J. Ma, “Learning spatial-spectral prior for super-resolution of hyperspectral imagery,” IEEE Trans. Computational Imaging, vol. 6, pp. 1082–1096, 2020. doi: 10.1109/TCI.2020.2996075
    [48]
    X. Wang, J. Ma, and J. Jiang, “Hyperspectral image super-resolution via recurrent feedback embedding and spatial—Spectral consistency regularization,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–13, 2022.
    [49]
    Y. Li, Z. Du, S. Wu, Y. Wang, Z. Wang, X. Zhao, and F. Zhang, “Progressive split-merge super resolution for hyperspectral imagery with group attention and gradient guidance,” ISPRS J. Photogrammetry Remote Sensing, vol. 182, pp. 14–36, 2021. doi: 10.1016/j.isprsjprs.2021.09.023
    [50]
    C. Zhang, M. Zhang, Y. Li, X. Gao, and S. Qiu, “Difference curvature multidimensional network for hyperspectral image super-resolution,” Remote Sensing, vol. 13, no. 17, p. 3455, 2021. doi: 10.3390/rs13173455
    [51]
    J. Hu, Y. Tang, and S. Fan, “Hyperspectral image super resolution based on multiscale feature fusion and aggregation network with 3-D convolution,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 13, pp. 5180–5193, 2020. doi: 10.1109/JSTARS.2020.3020890
    [52]
    D. Liu, J. Li, and Q. Yuan, “A spectral grouping and attention-driven residual dense network for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 59, no. 9, pp. 7711–7725, 2021. doi: 10.1109/TGRS.2021.3049875
    [53]
    J. Hu, X. Jia, Y. Li, G. He, and M. Zhao, “Hyperspectral image super-resolution via intrafusion network,” IEEE Trans. Geoscience Remote Sensing, vol. 58, no. 10, pp. 7459–7471, 2020. doi: 10.1109/TGRS.2020.2982940
    [54]
    Q. Wang, Q. Li, and X. Li, “Hyperspectral image superresolution using spectrum and feature context,” IEEE Trans. Industrial Electronics, vol. 68, no. 11, pp. 11 276–11 285, 2021. doi: 10.1109/TIE.2020.3038096
    [55]
    Z. Tang, Q. Xu, P. Wu, Z. Shi, and B. Pan, “Feedback refined local-global network for super-resolution of hyperspectral imagery,” Remote Sensing, vol. 14, no. 8, p. 1944, 2022. doi: 10.3390/rs14081944
    [56]
    X. Wang, Q. Hu, J. Jiang, and J. Ma, “A group-based embedding learning and integration network for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–16, 2022.
    [57]
    X. Wang, Y. Cheng, X. Mei, J. Jiang, and J. Ma, “Group shuffle and spectral-spatial fusion for hyperspectral image super-resolution,” IEEE Trans. Computational Imaging, vol. 8, pp. 1223–1236, 2022. doi: 10.1109/TCI.2023.3235153
    [58]
    S. Mei, X. Yuan, J. Ji, S. Wan, J. Hou, and Q. Du, “Hyperspectral image super-resolution via convolutional neural network,” in Proc. IEEE Int. Conf. Image Processing, 2017, pp. 4297–4301.
    [59]
    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 Sensing, vol. 9, no. 11, p. 1139, 2017. doi: 10.3390/rs9111139
    [60]
    J. Li, R. Cui, B. Li, Y. Li, S. Mei, and Q. Du, “Dual 1D-2D spatial-spectral cnn for hyperspectral image super-resolution,” in Proc. IEEE Int. Geoscience Remote Sensing Symposium, 2019, pp. 3113–3116.
    [61]
    J. Li, R. Cui, B. Li, R. Song, Y. Li, and Q. Du, “Hyperspectral image super-resolution with 1D-2D attentional convolutional neural network,” Remote Sensing, vol. 11, no. 23, p. 2859, 2019. doi: 10.3390/rs11232859
    [62]
    Q. Wang, Q. Li, and X. Li, “Spatial-spectral residual network for hyperspectral image super-resolution,” arXiv preprint arXiv: 2001.04609, 2020.
    [63]
    Q. Li, Q. Wang, and X. Li, “Mixed 2D/3D convolutional network for hyperspectral image super-resolution,” Remote Sensing, vol. 12, no. 10, p. 1660, 2020.
    [64]
    Q. Li, Q. Wang, and X. Li, “Exploring the relationship between 2D/3D convolution for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 59, no. 10, pp. 8693–8703, 2021. doi: 10.1109/TGRS.2020.3047363
    [65]
    Q. Li, Y. Yuan, and Q. Wang, “Hyperspectral image super-resolution via multi-domain feature learning,” Neurocomputing, vol. 472, pp. 85–94, 2022. doi: 10.1016/j.neucom.2021.10.041
    [66]
    Y. Fu, Z. Liang, and S. You, “Bidirectional 3D quasi-recurrent neural network for hyperspectral image super-resolution,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 14, pp. 2674–2688, 2021. doi: 10.1109/JSTARS.2021.3057936
    [67]
    J. Yang, L. Xiao, Y.-Q. Zhao, and J. C.-W. Chan, “Hybrid local and nonlocal 3-D attentive CNN for hyperspectral image super-resolution,” IEEE Geoscience Remote Sensing Letters, vol. 18, no. 7, pp. 1274–1278, 2021. doi: 10.1109/LGRS.2020.2997092
    [68]
    Q. Huang, W. Li, T. Hu, and R. Tao, “Hyperspectral image super-resolution using generative adversarial network and residual learning,” in Proc. IEEE Int. Conf. Acoustics, Speech Signal Processing, 2019, pp. 3012–3016.
    [69]
    X. Dou, C. Li, Q. Shi, and M. Liu, “Super-resolution for hyperspectral remote sensing images based on the 3D attention−SRGAN network,” Remote Sensing, vol. 12, no. 7, p. 1204, 2020. doi: 10.3390/rs12071204
    [70]
    C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2017, pp. 4681–4690.
    [71]
    J. Li, R. Cui, B. Li, R. Song, Y. Li, Y. Dai, and Q. Du, “Hyperspectral image super-resolution by band attention through adversarial learning,” IEEE Trans. Geoscience Remote Sensing, vol. 58, no. 6, pp. 4304–4318, 2020. doi: 10.1109/TGRS.2019.2962713
    [72]
    R. Jiang, X. Li, A. Gao, L. Li, H. Meng, S. Yue, and L. Zhang, “Learning spectral and spatial features based on generative adversarial network for hyperspectral image super-resolution,” in Proc. IEEE Int. Geoscience Remote Sensing Symposium, 2019, pp. 3161–3164.
    [73]
    B. Wang, S. Zhang, Y. Feng, S. Mei, S. Jia, and Q. Du, “Hyperspectral imagery spatial super-resolution using generative adversarial network,” IEEE Trans. Computational Imaging, vol. 7, pp. 948–960, 2021. doi: 10.1109/TCI.2021.3110103
    [74]
    Y. Shi, L. Han, L. Han, S. Chang, T. Hu, and D. Dancey, “A latent encoder coupled generative adversarial network (LE-GAN) for efficient hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–19, 2022.
    [75]
    D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2018, pp. 9446–9454.
    [76]
    O. Sidorov and J. Y. Hardeberg, “Deep hyperspectral prior: Single-image denoising, inpainting, super-resolution,” in Proc. IEEE Int. Conf. Computer Vision Workshops, 2019.
    [77]
    Z. Gong, N. Wang, D. Cheng, X. Jiang, J. Xin, X. Yang, and X. Gao, “Learning deep resonant prior for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–14, 2022.
    [78]
    Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image super-resolution,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2018, pp. 2472–2481.
    [79]
    M. Zhao, J. Ning, J. Hu, and T. Li, “Hyperspectral image super-resolution under the guidance of deep gradient information,” Remote Sensing, vol. 13, no. 12, p. 2382, 2021. doi: 10.3390/rs13122382
    [80]
    B. Wang, S. Mei, Y. Feng, and Q. Du, “Hyperspectral imagery super-resolution based on self-calibrated attention residual network,” in Proc. IEEE Int. Geoscience Remote Sensing Symposium, 2021, pp. 3896–3899.
    [81]
    X. Wang, J. Ma, J. Jiang, and X.-P. Zhang, “Dilated projection correction network based on autoencoder for hyperspectral image super-resolution,” Neural Networks, vol. 146, pp. 107–119, 2022. doi: 10.1016/j.neunet.2021.11.014
    [82]
    J. Zhang, M. Shao, Z. Wan, and Y. Li, “Multi-scale feature mapping network for hyperspectral image super-resolution,” Remote Sensing, vol. 13, no. 20, p. 4180, 2021. doi: 10.3390/rs13204180
    [83]
    J. Hou, Z. Zhu, J. Hou, H. Zeng, J. Wu, and J. Zhou, “Deep posterior distribution-based embedding for hyperspectral image super-resolution,” IEEE Trans. Image Processing, vol. 31, pp. 5720–5732, 2022. doi: 10.1109/TIP.2022.3201478
    [84]
    D. Liu, J. Li, Q. Yuan, L. Zheng, J. He, S. Zhao, and Y. Xiao, “An efficient unfolding network with disentangled spatial-spectral representation for hyperspectral image super-resolution,” Information Fusion, vol. 94, pp. 92–111, 2023. doi: 10.1016/j.inffus.2023.01.018
    [85]
    Y. Liu, J. Hu, X. Kang, J. Luo, and S. Fan, “Interactformer: Interactive transformer and CNN for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–15, 2022.
    [86]
    C. Deng, X. Luo, and W. Wang, “Multiple frame splicing and degradation learning for hyperspectral imagery super-resolution,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 15, pp. 8389–8401, 2022. doi: 10.1109/JSTARS.2022.3207777
    [87]
    B. Aiazzi, S. Baronti, and M. Selva, “Improving component substitution pansharpening through multivariate regression of MS + PAN data,” IEEE Trans. Geoscience Remote Sensing, vol. 45, no. 10, pp. 3230–3239, 2007. doi: 10.1109/TGRS.2007.901007
    [88]
    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. Geoscience Remote Sensing, vol. 46, no. 5, pp. 1301–1312, 2008. doi: 10.1109/TGRS.2007.912448
    [89]
    K. Li, W. Xie, Q. Du, and Y. Li, “DDLPS: Detail-based deep Laplacian pansharpening for hyperspectral imagery,” IEEE Trans. Geoscience Remote Sensing, vol. 57, no. 10, pp. 8011–8025, 2019. doi: 10.1109/TGRS.2019.2917759
    [90]
    W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Deep Laplacian pyramid networks for fast and accurate super-resolution,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2017, pp. 624–632.
    [91]
    W. Dong, T. Zhang, J. Qu, S. Xiao, J. Liang, and Y. Li, “Laplacian pyramid dense network for hyperspectral pansharpening,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–13, 2022.
    [92]
    W. Xie, J. Lei, Y. Cui, Y. Li, and Q. Du, “Hyperspectral pansharpening with deep priors,” IEEE Trans. Neural Networks Learning Systems, vol. 31, no. 5, pp. 1529–1543, 2020. doi: 10.1109/TNNLS.2019.2920857
    [93]
    G. He, J. Zhong, J. Lei, Y. Li, and W. Xie, “Hyperspectral pansharpening based on spectral constrained adversarial autoencoder,” Remote Sensing, vol. 11, no. 22, p. 2691, 2019. doi: 10.3390/rs11222691
    [94]
    W. Dong, Y. Yang, J. Qu, W. Xie, and Y. Li, “Fusion of hyperspectral and panchromatic images using generative adversarial network and image segmentation,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–13, 2022.
    [95]
    Y. Zheng, J. Li, Y. Li, K. Cao, and K. Wang, “Deep residual learning for boosting the accuracy of hyperspectral pansharpening,” IEEE Geoscience Remote Sensing Letters, vol. 17, no. 8, pp. 1435–1439, 2020. doi: 10.1109/LGRS.2019.2945424
    [96]
    W. Dong, C. Zhou, F. Wu, J. Wu, G. Shi, and X. Li, “Model-guided deep hyperspectral image super-resolution,” IEEE Trans. Image Processing, vol. 30, pp. 5754–5768, 2021. doi: 10.1109/TIP.2021.3078058
    [97]
    X. Lu, J. Zhang, D. Yang, L. Xu, and F. Jia, “Cascaded convolutional neural network-based hyperspectral image resolution enhancement via an auxiliary panchromatic image,” IEEE Trans. Image Processing, vol. 30, pp. 6815–6828, 2021. doi: 10.1109/TIP.2021.3098246
    [98]
    Y. Zheng, J. Li, Y. Li, Y. Shi, and J. Qu, “Deep residual spatial attention network for hyperspectral pansharpening,” in Proc. IEEE Int. Geoscience Remote Sensing Symposium, 2020, pp. 2671–2674.
    [99]
    X. Wu, J. Feng, R. Shang, X. Zhang, and L. Jiao, “Multi-objective guided divide-and-conquer network for hyperspectral pansharpening,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–17, 2022.
    [100]
    Y. Zheng, J. Li, Y. Li, J. Guo, X. Wu, and J. Chanussot, “Hyperspectral pansharpening using deep prior and dual attention residual network,” IEEE Trans. Geoscience Remote Sensing, vol. 58, no. 11, pp. 8059–8076, 2020. doi: 10.1109/TGRS.2020.2986313
    [101]
    W. G. C. Bandara, J. M. J. Valanarasu, and V. M. Patel, “Hyperspectral pansharpening based on improved deep image prior and residual reconstruction,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–16, 2022.
    [102]
    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 multi-detail extractor and spectral attention for hyperspectral pansharpening,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 15, pp. 7539–7555, 2022. doi: 10.1109/JSTARS.2022.3202866
    [103]
    L. He, J. Zhu, J. Li, A. Plaza, J. Chanussot, and B. Li, “Hyperpnn: Hyperspectral pansharpening via spectrally predictive convolutional neural networks,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 12, no. 8, pp. 3092–3100, 2019. doi: 10.1109/JSTARS.2019.2917584
    [104]
    W. Xie, Y. Cui, Y. Li, J. Lei, Q. Du, and J. Li, “HPGAN: Hyperspectral pansharpening using 3-D generative adversarial networks,” IEEE Trans. Geoscience Remote Sensing, vol. 59, no. 1, pp. 463–477, 2021. doi: 10.1109/TGRS.2020.2994238
    [105]
    S. Hou, S. Xiao, W. Dong, and J. Qu, “Multi-level features fusion via cross-layer guided attention for hyperspectral pansharpening,” Neurocomputing, vol. 506, pp. 380–392, 2022. doi: 10.1016/j.neucom.2022.07.071
    [106]
    P. Guan and E. Y. Lam, “Multistage dual-attention guided fusion network for hyperspectral pansharpening,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–14, 2022.
    [107]
    J. Qu, Z. Xu, W. Dong, S. Xiao, Y. Li, and Q. Du, “A spatio-spectral fusion method for hyperspectral images using residual hyper-dense network,” IEEE Trans. Neural Networks Learning Systems, pp. 1–15, 2022.
    [108]
    Y. Yang, W. Dong, S. Xiao, T. Zhang, and J. Qu, “Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening,” Optics Letters, vol. 47, no. 6, pp. 1371–1374, 2022. doi: 10.1364/OL.447405
    [109]
    Y. Zheng, J. Li, Y. Li, K. Cao, and K. Wang, “Pansharpening of hyperspectral images with detail guided feature modulation,” in Proc. IEEE Int. Geoscience Remote Sensing Symposium, 2021, pp. 4456–4459.
    [110]
    Y. Li, Y. Zheng, J. Li, R. Song, and J. Chanussot, “Hyperspectral pansharpening with adaptive feature modulation-based detail injection network,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–17, 2022.
    [111]
    Y. Shang, J. Liu, J. Yang, and Z. Wu, “A model-inspired approach with transformers for hyperspectral pansharpening,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 15, pp. 7187–7202, 2022. doi: 10.1109/JSTARS.2022.3199207
    [112]
    W. G. C. Bandara and V. M. Patel, “Hypertransformer: A textural and spectral feature fusion transformer for pansharpening,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2022, pp. 1767–1777.
    [113]
    J. Ma, L. Tang, F. Fan, J. Huang, X. Mei, and Y. Ma, “SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer,” IEEE/CAA J. Automa. Sinica, vol. 9, no. 7, pp. 1200–1217, 2022. doi: 10.1109/JAS.2022.105686
    [114]
    J. Qu, Y. Shi, W. Xie, Y. Li, X. Wu, and Q. Du, “MSSL: Hyperspectral and panchromatic images fusion via multiresolution spatial – spectral feature learning networks,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–13, 2022.
    [115]
    R. Zhao and S. Du, “An encoder-decoder with a residual network for fusing hyperspectral and panchromatic remote sensing images,” Remote Sensing, vol. 14, no. 9, p. 1981, 2022. doi: 10.3390/rs14091981
    [116]
    R. Zhao and S. Du, “Spectral-spatial residual network for fusing hyperspectral and panchromatic remote sensing images,” Remote Sensing, vol. 14, no. 3, p. 800, 2022. doi: 10.3390/rs14030800
    [117]
    J. Qu, S. Hou, W. Dong, S. Xiao, Q. Du, and Y. Li, “A dual – Branch detail extraction network for hyperspectral pansharpening,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–13, 2022.
    [118]
    W. Dong, J. Qu, T. Zhang, Y. Li, and Q. Du, “Context-aware guided attention based cross-feedback dense network for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–14, 2022.
    [119]
    L. He, J. Zhu, J. Li, A. Plaza, J. Chanussot, and Z. Yu, “CNN-based hyperspectral pansharpening with arbitrary resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–21, 2022.
    [120]
    L. He, J. Xie, J. Li, A. Plaza, J. Chanussot, and J. Zhu, “Variable subpixel convolution based arbitrary-resolution hyperspectral pansharpening,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–19, 2022.
    [121]
    W. Dong, S. Hou, S. Xiao, J. Qu, Q. Du, and Y. Li, “Generative dual-adversarial network with spectral fidelity and spatial enhancement for hyperspectral pansharpening,” IEEE Trans. Neural Networks Learning Systems, vol. 33, no. 12, pp. 7303–7317, 2022. doi: 10.1109/TNNLS.2021.3084745
    [122]
    J. Nie, Q. Xu, and J. Pan, “Unsupervised hyperspectral pansharpening by ratio estimation and residual attention network,” IEEE Geoscience Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
    [123]
    L. He, J. Zhu, J. Li, D. Meng, J. Chanussot, and A. Plaza, “Spectral-fidelity convolutional neural networks for hyperspectral pansharpening,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 13, pp. 5898–5914, 2020. doi: 10.1109/JSTARS.2020.3025040
    [124]
    W. Wei, J. Nie, Y. Li, L. Zhang, and Y. Zhang, “Deep recursive network for hyperspectral image super-resolution,” IEEE Trans. Computational Imaging, vol. 6, pp. 1233–1244, 2020. doi: 10.1109/TCI.2020.3014451
    [125]
    R. Dian, S. Li, and X. Kang, “Regularizing hyperspectral and multispectral image fusion by cnn denoiser,” IEEE Trans. Neural Networks Learning Systems, vol. 32, no. 3, pp. 1124–1135, 2021. doi: 10.1109/TNNLS.2020.2980398
    [126]
    Q. Xie, M. Zhou, Q. Zhao, D. Meng, W. Zuo, and Z. Xu, “Multispectral and hyperspectral image fusion by MS/HS fusion net,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2019, pp. 1585–1594.
    [127]
    W. Wang, W. Zeng, Y. Huang, X. Ding, and J. Paisley, “Deep blind hyperspectral image fusion,” in Proc. IEEE Int. Conf. Computer Vision, 2019, pp. 4150–4159.
    [128]
    W. Wang, X. Fu, W. Zeng, L. Sun, R. Zhan, Y. Huang, and X. Ding, “Enhanced deep blind hyperspectral image fusion,” IEEE Trans. Neural Networks Learning Systems, vol. 34, no. 3, pp. 1513–1523, 2023. doi: 10.1109/TNNLS.2021.3105543
    [129]
    Z. Liu, Y. Zheng, and X.-H. Han, “Deep unsupervised fusion learning for hyperspectral image super resolution,” Sensors, vol. 21, no. 7, p. 2348, 2021. doi: 10.3390/s21072348
    [130]
    R. Dian, S. Li, A. Guo, and L. Fang, “Deep hyperspectral image sharpening,” IEEE Trans. Neural Networks Learning Systems, vol. 29, no. 11, pp. 5345–5355, 2018. doi: 10.1109/TNNLS.2018.2798162
    [131]
    X. Wang, J. Chen, Q. Wei, and C. Richard, “Hyperspectral image super-resolution via deep prior regularization with parameter estimation,” IEEE Trans. Circuits Systems Video Technology, vol. 32, no. 4, pp. 1708–1723, 2022. doi: 10.1109/TCSVT.2021.3078559
    [132]
    M. Vella, B. Zhang, W. Chen, and J. F. Mota, “Enhanced hyperspectral image super-resolution via RGB fusion and TV-TV minimization,” in Proc. IEEE Int. Conf. Image Processing, 2021, pp. 3837–3841.
    [133]
    X. Wang, J. Chen, and C. Richard, “Hyperspectral image super-resolution with deep priors and degradation model inversion,” in Proc. IEEE Int. Conf. Acoustics, Speech Signal Processing, 2022, pp. 2814–2818.
    [134]
    J. Liu, D. Shen, Z. Wu, L. Xiao, J. Sun, and H. Yan, “Patch-aware deep hyperspectral and multispectral image fusion by unfolding subspace-based optimization model,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 15, pp. 1024–1038, 2022. doi: 10.1109/JSTARS.2022.3140211
    [135]
    J. Yang, L. Xiao, Y.-Q. Zhao, and J. C.-W. Chan, “Variational regularization network with attentive deep prior for hyperspectral-ltispectral image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–17, 2022.
    [136]
    W. Wan, B. Zhang, M. Vella, J. F. Mota, and W. Chen, “Robust rgbguided super-resolution of hyperspectral images via tv3 minimization,” IEEE Signal Processing Letters, vol. 29, pp. 957–961, 2022. doi: 10.1109/LSP.2022.3159149
    [137]
    T. Huang, W. Dong, J. Wu, L. Li, X. Li, and G. Shi, “Deep hyperspectral image fusion network with iterative spatio-spectral regularization,” IEEE Trans. Computational Imaging, vol. 8, pp. 201–214, 2022. doi: 10.1109/TCI.2022.3152700
    [138]
    K. Zheng, L. Gao, W. Liao, D. Hong, B. Zhang, X. Cui, and J. Chanussot, “Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 59, no. 3, pp. 2487–2502, 2021. doi: 10.1109/TGRS.2020.3006534
    [139]
    J. Gao, J. Li, and M. Jiang, “Hyperspectral and multispectral image fusion by deep neural network in a self-supervised manner,” Remote Sensing, vol. 13, no. 16, p. 3226, 2021. doi: 10.3390/rs13163226
    [140]
    Z. Liu, Y. Zheng, and X.-H. Han, “Unsupervised multispectral and hyperspectral image fusion with deep spatial and spectral priors,” in Proc. Asian Conf. Computer Vision, p. 12628, 2020.
    [141]
    L. Zhang, J. Nie, W. Wei, Y. Zhang, S. Liao, and L. Shao, “Unsupervised adaptation learning for hyperspectral imagery super-resolution,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2020, pp. 3073–3082.
    [142]
    Y. Fu, T. Zhang, Y. Zheng, D. Zhang, and H. Huang, “Hyperspectral image super-resolution with optimized RGB guidance,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2019, pp. 11661–11670.
    [143]
    W. Wei, J. Nie, L. Zhang, and Y. Zhang, “Unsupervised recurrent hyperspectral imagery super-resolution using pixel-aware refinement,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–15, 2022.
    [144]
    Y. Qu, H. Qi, and C. Kwan, “Unsupervised sparse dirichlet-net for hyperspectral image super-resolution,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2018, pp. 2511–2520.
    [145]
    Z. Wang, B. Chen, R. Lu, H. Zhang, H. Liu, and P. K. Varshney, “FusionNet: An unsupervised convolutional variational network for hyperspectral and multispectral image fusion,” IEEE Trans. Image Processing, vol. 29, pp. 7565–7577, 2020. doi: 10.1109/TIP.2020.3004261
    [146]
    Y. Qu, H. Qi, C. Kwan, N. Yokoya, and J. Chanussot, “Unsupervised and unregistered hyperspectral image super-resolution with mutual dirichlet-Net,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–18, 2022.
    [147]
    K. Zheng, L. Gao, D. Hong, B. Zhang, and J. Chanussot, “NonRegSRNet: A nonrigid registration hyperspectral super-resolution network,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–16, 2022.
    [148]
    J. Zhao, Y. Qu, S. Ninomiya, and W. Guo, “Endmember-assisted camera response function learning, toward improving hyperspectral image super-resolution performance,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–14, 2022.
    [149]
    J. Liu, Z. Wu, L. Xiao, and X.-J. Wu, “Model inspired autoencoder for unsupervised hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–12, 2022.
    [150]
    Z. Wang, B. Chen, H. Zhang, and H. Liu, “Unsupervised hyperspectral and multispectral images fusion based on nonlinear variational probabilistic generative model,” IEEE Trans. Neural Networks Learning Systems, vol. 33, no. 2, pp. 721–735, 2022. doi: 10.1109/TNNLS.2020.3028772
    [151]
    F. Palsson, J. R. Sveinsson, and M. O. Ulfarsson, “Multispectral and hyperspectral image fusion using a 3-D-convolutional neural network,” IEEE Geoscience Remote Sensing Letters, vol. 14, no. 5, pp. 639–643, 2017. doi: 10.1109/LGRS.2017.2668299
    [152]
    Z. Shi, C. Chen, Z. Xiong, D. Liu, Z.-J. Zha, and F. Wu, “Deep residual attention network for spectral image super-resolution,” in Proc. European Conf. Computer Vision Workshops, 2018.
    [153]
    F. Zhou, R. Hang, Q. Liu, and X. Yuan, “Pyramid fully convolutional network for hyperspectral and multispectral image fusion,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 12, no. 5, pp. 1549–1558, 2019. doi: 10.1109/JSTARS.2019.2910990
    [154]
    S. Huang and D. W. Messinger, “An unsupervised Laplacian pyramid network for radiometrically accurate data fusion of hyperspectral and multispectral imagery,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–17, 2022.
    [155]
    X.-H. Han and Y.-W. Chen, “Deep residual network of spectral and spatial fusion for hyperspectral image super-resolution,” in Proc. IEEE Int. Conf. Multimedia Big Data, 2019, pp. 266–270.
    [156]
    Z. Zhu, J. Hou, J. Chen, H. Zeng, and J. Zhou, “Hyperspectral image super-resolution via deep progressive zero-centric residual learning,” IEEE Trans. Image Processing, vol. 30, pp. 1423–1438, 2021. doi: 10.1109/TIP.2020.3044214
    [157]
    X.-H. Han, B. Shi, and Y. Zheng, “SSF-CNN: Spatial and spectral fusion with CNN for hyperspectral image super-resolution,” in Proc. IEEE Int. Conf. Image Processing, 2018, pp. 2506–2510.
    [158]
    J.-F. Hu, T.-Z. Huang, L.-J. Deng, T.-X. Jiang, G. Vivone, and J. Chanussot, “Hyperspectral image super-resolution via deep spatiospectral attention convolutional neural networks,” IEEE Trans. Neural Networks Learning Systems, vol. 33, no. 12, pp. 7251–7265, 2022. doi: 10.1109/TNNLS.2021.3084682
    [159]
    B. Pan, Q. Qu, X. Xu, and Z. Shi, “Structure – Color preserving network for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–12, 2022.
    [160]
    J. Xiao, J. Li, Q. Yuan, and L. Zhang, “A dual-UNET with multistage details injection for hyperspectral image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–13, 2022.
    [161]
    T. Zhan, Z. Bi, H. Wu, C. Xu, Q. Du, Y. Xu, and Z. Wu, “A novel cross-scale octave network for hyperspectral and multispectral image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–16, 2022.
    [162]
    Z. Guo, J. Xin, N. Wang, J. Li, and X. Gao, “External-internal attention for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–14, 2022.
    [163]
    S. Liu, S. Liu, S. Zhang, B. Li, W. Hu, and Y.-D. Zhang, “Ssau-Net: A spectral-spatial attention-based U-Net for hyperspectral image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–16, 2022.
    [164]
    X.-H. Han, Y. Zheng, and Y.-W. Chen, “Multi-level and multi-scale spatial and spectral fusion cnn for hyperspectral image super-resolution,” in Proc. IEEE Int. Conf. Computer Vision Workshops, 2019.
    [165]
    T. Uezato, D. Hong, N. Yokoya, and W. He, “Guided deep decoder: Unsupervised image pair fusion,” in Proc. European Conf. Computer Vision, 2020, pp. 87–102.
    [166]
    X. Wang, X. Wang, K. Zhao, X. Zhao, and C. Song, “Fsl-UNet: Full-scale linked UNet with spatial-spectral joint perceptual attention for hyperspectral and multispectral image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–14, 2022.
    [167]
    S. Xu, O. Amira, J. Liu, C.-X. Zhang, J. Zhang, and G. Li, “HAMMFN: Hyperspectral and multispectral image multiscale fusion network with RAP loss,” IEEE Trans. Geoscience Remote Sensing, vol. 58, no. 7, pp. 4618–4628, 2020. doi: 10.1109/TGRS.2020.2964777
    [168]
    R. Ran, L.-J. Deng, T.-X. Jiang, J.-F. Hu, J. Chanussot, and G. Vivone, “GuidedNet: A general cnn fusion framework via high-resolution guidance for hyperspectral image super-resolution,” IEEE Trans. Cybernetics, pp. 1–14, 2023. DOI: 10.1109/TCYB.2023.3238200
    [169]
    V. K. Ha, J. Ren, Z. Wang, G. Sun, H. Zhao, and S. Marshall, “Multiscale spatial fusion and regularization induced unsupervised auxiliary task CNN model for deep super-resolution of hyperspectral images,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 15, pp. 4583–4598, 2022. doi: 10.1109/JSTARS.2022.3176969
    [170]
    W. Wang, W. Zeng, Y. Huang, and X. Ding, “Deep multiscale feedback network for hyperspectral image fusion,” IEEE Geoscience Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
    [171]
    J. Yang, Y.-Q. Zhao, and J. C.-W. Chan, “Hyperspectral and multispectral image fusion via deep two-branches convolutional neural network,” Remote Sensing, vol. 10, no. 5, p. 800, 2018. doi: 10.3390/rs10050800
    [172]
    Q. Li, M. Gong, Y. Yuan, and Q. Wang, “Symmetrical feature propagation network for hyperspectral image super-resolution,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–12, 2022.
    [173]
    X. Zhang, W. Huang, Q. Wang, and X. Li, “SSR-Net: Spatial – Spectral reconstruction network for hyperspectral and multispectral image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 59, no. 7, pp. 5953–5965, 2021. doi: 10.1109/TGRS.2020.3018732
    [174]
    Y. Zheng, J. Li, Y. Li, J. Guo, X. Wu, Y. Shi, and J. Chanussot, “Edge-conditioned feature transform network for hyperspectral and multispectral image fusion,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–15, 2022.
    [175]
    W. Chen, X. Zheng, and X. Lu, “Hyperspectral image super-resolution with self-supervised spectral-spatial residual network,” Remote Sensing, vol. 13, no. 7, p. 1260, 2021. doi: 10.3390/rs13071260
    [176]
    H. Su, H. Jin, and C. Sun, “Deep pansharpening via 3D spectral super-resolution network and discrepancy-based gradient transfer,” Remote Sensing, vol. 14, no. 17, p. 4250, 2022. doi: 10.3390/rs14174250
    [177]
    J. Li, K. Zheng, J. Yao, L. Gao, and D. Hong, “Deep unsupervised blind hyperspectral and multispectral data fusion,” IEEE Geoscience Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
    [178]
    S. Liu, S. Miao, J. Su, B. Li, W. Hu, and Y.-D. Zhang, “UMAG-Net: A new unsupervised multiattention-guided network for hyperspectral and multispectral image fusion,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 14, pp. 7373–7385, 2021. doi: 10.1109/JSTARS.2021.3097178
    [179]
    J. Xiao, J. Li, Q. Yuan, M. Jiang, and L. Zhang, “Physics-based GAN with iterative refinement unit for hyperspectral and multispectral image fusion,” IEEE J. Selected Topics Applied Earth Observations Remote Sensing, vol. 14, pp. 6827–6841, 2021. doi: 10.1109/JSTARS.2021.3075727
    [180]
    Z. Zhang, Z. Xu, Z. Ahmed, A. Salekin, and T. Rahman, “Hyperspectral image super-resolution in arbitrary input-output band settings,” in Proc. IEEE Winter Conf. Applications Computer Vision, 2022, pp. 749–759.
    [181]
    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 Geoscience Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
    [182]
    T. You, C. Wu, Y. Bai, D. Wang, H. Ge, and Y. Li, “HMF-former: Spatio-spectral transformer for hyperspectral and multispectral image fusion,” IEEE Geoscience Remote Sensing Letters, vol. 20, pp. 1–5, 2023.
    [183]
    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. Geoscience Remote Sensing, vol. 61, pp. 1–15, 2023.
    [184]
    W. Sun, K. Ren, X. Meng, C. Xiao, G. Yang, and J. Peng, “A band divide-and-conquer multispectral and hyperspectral image fusion method,” IEEE Trans. Geoscience Remote Sensing, vol. 60, pp. 1–13, 2022.
    [185]
    B. Arad and O. Ben-Shahar, “Sparse recovery of hyperspectral signal from natural RGB images,” in Proc. European Conf. Computer Vision, 2016, pp. 19–34.
    [186]
    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 Processing, vol. 19, no. 9, pp. 2241–2253, 2010. doi: 10.1109/TIP.2010.2046811
    [187]
    N. Yokoya and A. Iwasaki, “Airborne hyperspectral data over chikusei,” Space Application Laboratory, University of Tokyo, Japan, Tech. Rep. SAL-2016-05-27, May 2016.
    [188]
    A. Chakrabarti and T. Zickler, “Statistics of real-world hyperspectral images,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2011, pp. 193–200.
    [189]
    F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The spectral image processing system (SIPS) interactive visualization and analysis of imaging spectrometer data,” Remote Sensing Environment, vol. 44, no. 2–3, pp. 145–163, 1993. doi: 10.1016/0034-4257(93)90013-N
    [190]
    Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81–84, 2002. doi: 10.1109/97.995823
    [191]
    Y. Zeng, W. Huang, M. Liu, H. Zhang, and B. Zou, “Fusion of satellite images in URBAN area: Assessing the quality of resulting images,” in Proc. Int. Conf. Geoinformatics, 2010, pp. 1–4.
    [192]
    Y. Zhong, X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang, “WHU-HI: Uav-borne hyperspectral with high spatial resolution (h2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF,” Remote Sensing Environment, vol. 250, p. 112012, 2020. doi: 10.1016/j.rse.2020.112012
    [193]
    J. Behmann, J. Steinrücken, and L. Plümer, “Detection of early plant stress responses in hyperspectral images,” ISPRS J. Photogrammetry Remote Sensing, vol. 93, pp. 98–111, 2014. doi: 10.1016/j.isprsjprs.2014.03.016
    [194]
    C. Gomez, P. Lagacherie, and G. Coulouma, “Regional predictions of eight common soil properties and their spatial structures from hyperspectral vis–NIR data,” Geoderma, vol. 189, pp. 176–185, 2012.
    [195]
    F. A. Kruse, J. W. Boardman, and J. F. Huntington, “Comparison of airborne hyperspectral data and EO-1 hyperion for mineral mapping,” IEEE Trans. Geoscience Remote Sensing, vol. 41, no. 6, pp. 1388–1400, 2003. doi: 10.1109/TGRS.2003.812908
    [196]
    A. Shafique, G. Cao, Z. Khan, M. Asad, and M. Aslam, “Deep learning-based change detection in remote sensing images: A review,” Remote Sensing, vol. 14, no. 4, p. 871, 2022. doi: 10.3390/rs14040871
    [197]
    Z. Shao, H. Fu, D. Li, O. Altan, and T. Cheng, “Remote sensing monitoring of multi-scale watersheds impermeability for Urban hydrological evaluation,” Remote Sensing Environment, vol. 232, p. 111338, 2019. doi: 10.1016/j.rse.2019.111338
    [198]
    N. Rudorff, C. M. Rudorff, M. Kampel, and G. Ortiz, “Remote sensing monitoring of the impact of a major mining wastewater disaster on the turbidity of the doce river plume off the eastern brazilian coast,” ISPRS J. Photogrammetry Remote Sensing, vol. 145, pp. 349–361, 2018. doi: 10.1016/j.isprsjprs.2018.02.013
    [199]
    C. Pohl and J. van Genderen, “Remote sensing image fusion: An update in the context of digital earth,” Int. J. Digital Earth, vol. 7, no. 2, pp. 158–172, 2014. doi: 10.1080/17538947.2013.869266
    [200]
    G. Lu and B. Fei, “Medical hyperspectral imaging: A review,” J. Biomedical Optics, vol. 19, no. 1, pp. 010901–010901, 2014. doi: 10.1117/1.JBO.19.1.010901
    [201]
    L. Wei, L. Meng, C. Tianhong, C. Zhaoyao, and T. Ran, “Application of a hyperspectral image in medical field: A review,” Image Graph, vol. 26, pp. 1764–1785, 2021.
    [202]
    R. Cui, H. Yu, T. Xu, X. Xing, X. Cao, K. Yan, and J. Chen, “Deep learning in medical hyperspectral images: A review,” Sensors, vol. 22, no. 24, p. 9790, 2022. doi: 10.3390/s22249790
    [203]
    J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, “Image matching from handcrafted to deep features: A survey,” Int. J. Computer Vision, vol. 129, pp. 23–79, 2021. doi: 10.1007/s11263-020-01359-2
    [204]
    X. Jiang, J. Ma, G. Xiao, Z. Shao, and X. Guo, “A review of multimodal image matching: Methods and applications,” Information Fusion, vol. 73, pp. 22–71, 2021. doi: 10.1016/j.inffus.2021.02.012
    [205]
    L. Tang, Y. Deng, Y. Ma, J. Huang, and J. Ma, “SuperFusion: A versatile image registration and fusion network with semantic awareness,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2121–2137, 2022. doi: 10.1109/JAS.2022.106082
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    Highlights

    • A comprehensive review of latest DL methods for HS image SR is provided
    • The commonly used hyperspectral datasets are summarized
    • Evaluations for HS image SR methods are performed in three categories
    • We provide challenges and future research directions for HS image SR methods

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