부산대학교 도서관

Hyperspectral super-resolution based on coupled Tucker decomposition has been recently considered in the remote sensing community. The state-of-the-art approaches did not fully exploit the coupling of information contained in hyperspectral and multispectral images of the same scene. This paper proposes a new algorithm that overcomes this limitation. It accounts for both the high-resolution and the low-resolution information in the model by solving a set of least-squares problems. In addition, we provide exact recovery conditions for the super-resolution image in the noiseless case. Our simulations show that the proposed algorithm achieves very good reconstruction quality with a very low computational complexity.