Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution

Yuanyang Bu

doi:10.1109/LGRS.2023.3339505

Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution

2024-01-01

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Jonathan Cheung-Wai Chan

DOI：10.1109/LGRS.2023.3339505

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Abstract

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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.

Keywords:

Tensors

Superresolution

Spatial resolution

Hyperspectral imaging

Dictionaries

Optimization

Learning systems

low-rankness

super-resolution

tensor subspace representation

Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution

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