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Geostationary satellite imagery has applications in climate and weatherforecasting, planning natural energy resources, and predicting extreme weatherevents. For precise and accurate prediction, higher spatial and temporalresolution of geostationary satellite imagery is important. Although recentgeostationary satellite resolution has improved, the long-term analysis ofclimate applications is limited to using multiple satellites from the past tothe present due to the different resolutions. To solve this problem, weproposed warp and refine network (WR-Net). WR-Net is divided into an opticalflow warp component and a warp image refinement component. We used the TV-L1algorithm instead of deep learning-based approaches to extract the optical flowwarp component. The deep-learning-based model is trained on the human-centricview of the RGB channel and does not work on geostationary satellites, which isgray-scale one-channel imagery. The refinement network refines the warped imagethrough a multi-temporal fusion layer. We evaluated WR-Net by interpolation oftemporal resolution at 4 min intervals to 2 min intervals in large-scale GK2Ageostationary meteorological satellite imagery. Furthermore, we applied WR-Netto the future frame prediction task and showed that the explicit use of opticalflow can help future frame prediction.

Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network