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Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution.
- Source :
- IEEE Geoscience & Remote Sensing Letters; 2024, Vol. 21, p1-5, 5p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1545598X
- Volume :
- 21
- Database :
- Complementary Index
- Journal :
- IEEE Geoscience & Remote Sensing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 174717926
- Full Text :
- https://doi.org/10.1109/LGRS.2023.3339505