A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 9
Issue 11
IEEE/CAA Journal of Automatica Sinica
| Citation: | G. Cheng, Z. F. Shao, J. M. Wang, X. Huang, and C. Y. Dang, “Dual-branch multi-level feature aggregation network for pansharpening,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 2023–2026, Nov. 2022. doi: 10.1109/JAS.2022.105956
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B. Xiao, B. Xu, X. Bi, and W. Li, “Global-feature encoding U-Net (GEU-Net) for multi-focus image fusion,” IEEE Trans. Image Processing, vol. 30, pp. 163–175, 2020.
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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. Selected Topics Applied Earth Observations and Remote Sensing, vol. 11, no. 3, pp. 978–989, Mar. 2018. doi: 10.1109/JSTARS.2018.2794888
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H. Zhang and J. Ma, “GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening,” ISPRS J. Photogrammetry and Remote Sensing, vol. 172, pp. 223–239, 2021. doi: 10.1016/j.isprsjprs.2020.12.014
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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. Computer Vision and Pattern Recognition, 2021, pp. 1366–1375.
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J. Liu, W. Zhang, Y. Tang, J. Tang, and G. Wu, “Residual feature aggregation network for image super-resolution,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2020, pp. 2359–2368.
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M. Jiang, H. Shen, J. Li, Q. Yuan, and L. Zhang, “A differential information residual convolutional neural network for pansharpening,” ISPRS J. Photogrammetry and Remote Sensing, vol. 163, pp. 257–271, 2020. doi: 10.1016/j.isprsjprs.2020.03.006
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Y. Wang, L.-J. Deng, T.-J. Zhang, and X. Wu, “SS-Conv: Explicit spectral-to-spatial convolution for pansharpening,” in Proc. 29th ACM Int. Conf. Multimedia, 2021, pp. 4472–4480.
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