Video Satellite Imagery Super-Resolution via a Deep Residual Network

Recently, as a new remote sensing system, video satellite develops rapidly for long-time observation. Thanks to its high temporal resolution, video satellite has been extensively used for environmental detection, especially for dynamic target monitoring. However, limited by the imaging device, it sacrifices some of its spatial resolution. Therefore, the super-resolution (SR) technology applied to these images is crucial. Based on deep residual learning, which has obtained a great success in the single-image SR, we propose a SR network structure which consists of two main steps. First, we use multi-scale feature extraction to exploit more contextual information on video satellite imagery, which is aimed at inferring high frequency components. Then, we utilize a series of residual blocks to learn the mapping between low resolution and high resolution images in a deeper and more stable network. In our experiment, the SR reconstruction results on Jinlin-1 satellite images greatly indicate the effectiveness of our method and the potential of the residual network for video satellite imagery SR.