Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution

Yuanyang Bu, Yongqiang Zhao, Jize Xue, Jiaxin Yao, Jonathan Cheung Wai Chan

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8 引用 (Scopus)

摘要

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.

源语言英语
文章编号5501005
页(从-至)1-5
页数5
期刊IEEE Geoscience and Remote Sensing Letters
21
DOI
出版状态已出版 - 2024

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