Publication Details
Yuangyang Bu, Yongqiang Zhao, Jize Xue, Jiaxin Yao, Jonathan C-W Chan

IEEE Geoscience and Remote Sensing Letters

Contribution To Journal


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.

DOI scopus