HYPERSPECTRAL IMAGES SUPER-RESOLUTION ALGORITHMS BASED ON SPECTRAL SUBSPACE SPARSE TENSOR FACTORIZATION

Hyperspectral image super resolution (HSI-SR) problem aims to fuse a low-resolution hyperspectral image (HSI) with its corresponding multispectral image (MSI) to obtain a high-resolution hyperspectral image (HSR). However, the commonly used methods have some limitations. For example, the matrix decomposition method does not preserve the spatial or spectral information of the image well, and the tensor decomposition method has a high computational complexity. This paper proposes a method based on spectral subspace sparse tensor factorization (SSTF), which learns the spectral subspace from hyperspectral images, constrains this model using sparse tensor regularisation, transforms it to solve a convex optimisation problem, and iteratively optimises the problem using the alternating direction method of multipliers (ADMM). The computational complexity of the algorithm is effectively reduced while retaining spatial and spectral features. Compared with the state-of-the-art methods, experimental results demonstrate the effectiveness of the SSTF method.