Application of Bayesian super-resolution imaging algorithm to micro–nano satellite images
Haixing Yu, Mingquan Wang, Lingyu Xu, Maohua Wang · Journal of Applied Remote Sensing · 2019
The need for high-resolution imaging becomes particularly important in remote sensing image applications, such as ground-object identification. We introduce a Bayesian multiframe super-resolution algorithm that efficiently improves the imaging resolution of our micro–nano carbon satellite images. We begin by presenting a theoretical overview of the algorithm, and subsequently we conduct experiments in two phases. In the first phase, we compare the performance of our algorithm with those of other similar algorithms using a set of reference images. In the second practical application phase, we apply all the algorithms considered to panchromatic images obtained from our civilian micro–nano carbon satellite. Our results indicate that the proposed algorithm significantly outperforms the other algorithms, affording higher image resolution and greater image detail. We believe that our approach can contribute to the further development of satellite imaging systems.