Deep-learning-based remote sensing video super-resolution for Jilin-1 satellite

Due to far imaging distance and relatively harsh imaging conditions, the spatial resolution of remote sensing data are relatively low. Images/videos super-resolution is of great significance to effectively improve the spatial resolution and visual effect of remote sensing data. In this paper, we propose a deep-learning-based video super-resolution method for Jilin-1 remote sensing satellite. We use explicit motion compensation method by calculating the optical flow through the optical flow estimation network and compensating the motion of the image through warp operation. After obtaining the multi-frame images after motion compensation, it is necessary to use multi-frame image fusion for super-resolution reconstruction. We performed super-resolution experiments with scale factor 4 on Jilin-1 video dataset. In order to explore suitable fusion method, we compared two kinds of image fusion methods in the super-resolution network, i.e. concatenation by channel and 3D convolution, without motion compensation. Experimental results show that 3D convolution achieves better super-resolution performance, and video super-resolution result is better than the compared single image super-resolution method. We also performed experiments with motion compensation by optical flow estimation network. Experimental results show that the difference between the image after motion compensation and the reference frame becomes smaller. This indicates that the explicit motion compensation method can compensate the difference between the frames due to the target motion to a certain extent.