In real hyperspectral scenes, heterogeneous spatial details and noises make a single subspace assumptions unrealistic. In this letter, a novel transferable multiple tensor subspace learning scheme is proposed for super-resolution enhancement of hyperspectral image (HSI). The intrinsic assumption is that the nonlocal patch tensors extracted from HSIs are derived from multiple tensor low-rank subspaces, which is compatible with practical data distribution and may better characterize the complex structures underlying HSIs. The transferable subspace structures are embedded into both nonblind and semi-blind HSI super-resolution. The alternating direction method of multipliers (ADMMs) algorithm is derived for model learning. The superiority of our method is demonstrated by comprehensive experiments on both synthetic and real datasets.
Bu, Y, Zhao, Y, Xue, J, Yao, J & Chan, JC-W 2024, 'Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution', IEEE Geoscience and Remote Sensing Letters, vol. 21, 5501005, pp. 1-5. https://doi.org/10.1109/LGRS.2023.3339505
Bu, Y., Zhao, Y., Xue, J., Yao, J., & Chan, J. C.-W. (2024). Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution. IEEE Geoscience and Remote Sensing Letters, 21, 1-5. Article 5501005. https://doi.org/10.1109/LGRS.2023.3339505
@article{8304f9a3ab144b909e6be11fd6acb0ac,
title = "Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution",
abstract = "In real hyperspectral scenes, heterogeneous spatial details and noises make a single subspace assumptions unrealistic. In this letter, a novel transferable multiple tensor subspace learning scheme is proposed for super-resolution enhancement of hyperspectral image (HSI). The intrinsic assumption is that the nonlocal patch tensors extracted from HSIs are derived from multiple tensor low-rank subspaces, which is compatible with practical data distribution and may better characterize the complex structures underlying HSIs. The transferable subspace structures are embedded into both nonblind and semi-blind HSI super-resolution. The alternating direction method of multipliers (ADMMs) algorithm is derived for model learning. The superiority of our method is demonstrated by comprehensive experiments on both synthetic and real datasets. ",
author = "Yuangyang Bu and Yongqiang Zhao and Jize Xue and Jiaxin Yao and Chan, \{Jonathan Cheung-Wai\}",
note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61771391; in part by the Key Research and Development Plan of Shaanxi Province under Grant 2020ZDLGY07-11; in part by the Science Technology and Innovation Commission of Shenzhen Municipality under Grant JCYJ2017-0815162956949 and Grant JCYJ20180306171146740; and in part by the Doctor Dissertation of Northwestern Polytechnical University under Grant CX2021081. Publisher Copyright: {\textcopyright} 2023 IEEE.",
year = "2024",
doi = "10.1109/LGRS.2023.3339505",
language = "English",
volume = "21",
pages = "1--5",
journal = "IEEE Geoscience and Remote Sensing Letters",
issn = "1545-598X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}