Satellite Video Super-Resolution via Multiscale Deformable Convolution Alignment and Temporal Grouping Projection

As a new earth observation tool, satellite video has been widely used in remote-sensing field for dynamic analysis. Video super-resolution (VSR) technique has thus attracted increasing attention due to its improvement to spatial resolution of satellite video. However, the difficulty of remote-sensing image alignment and the low efficiency of spatial-temporal information fusion make poor generalization of the conventional VSR methods applied to satellite videos. In this article, a novel fusion strategy of temporal grouping projection and an accurate alignment module are proposed for satellite VSR. First, we propose a deformable convolution alignment module with a multiscale residual block to alleviate the alignment difficulties caused by scarce motion and various scales of moving objects in remote-sensing images. Second, a temporal grouping projection fusion strategy is proposed, which can reduce the complexity of projection and make the spatial features of reference frames play a continuous guiding role in spatial-temporal information fusion. Finally, a temporal attention module is designed to adaptively learn the different contributions of temporal information extracted from each group. Extensive experiments on Jilin-1 satellite video demonstrate that our method is superior to current state-of-the-art VSR methods.

Automated summary

A novel fusion strategy of temporal grouping projection and an accurate alignment module is proposed for satellite video super-resolution (VSR) in this article. By enhancing the alignment and fusion methods, the spatial resolution and dynamic analysis of satellite videos are effectively improved. Extensive experiments demonstrate that this method outperforms current state-of-the-art VSR methods on Jilin-1 satellite video.