Hyperspectral image super-resolution using sparse spectral unmixing and low-rank constraints

摘要

Hyperspectral images play an important role in real-world applications, such as recognition and remote sensing, etc. How to enhance the spatial resolution of hyperspectral image is still a challenging problem in this field. In this paper, we propose a novel hyperspectral image super-resolution approach by jointly incorporating the sparse, low-rank constraints and spectral mixture priori into a linear unmixing framework, which will make the unmixing framework more consistent with the real-world scenarios of the spectral mixture. Experiments on two public databases show that our proposed approach achieves much lower average reconstruction errors than other state-of-the-art methods.