Video Satellite Imagery Super Resolution for ‘Jilin-1’ via a Single-and-Multi Frame Ensembled Framework
Shu Zhang, Qiangqiang Yuan, Jie Li · 2020
Compared with traditional remote sensing images, satellite remote sensing video contains more useful information and can capture continuous dynamic video. Recently, many deep-learning based methods have been proposed for video super resolution. However, these methods tend to ignore the structural information and characteristics for video satellite imagery such as small ground targets, a wide range of scales and weak textures. To this end, this paper proposes a single-and-multi-frame ensembled framework called SMFE for remote sensing videos super-resolution. The SMFE framework combines a non-local based single image super resolution (SISR) network and a state-of-the-arts multi-frame super resolution (MFSR) network EDVR. Experiments have been performed to demonstrate the effectiveness of the proposed method on Jilin-1.