Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. Although recent geostationary satellite resolution has improved, the long-term analysis of climate applications is limited to using multiple satellites from the past to the present due to the different resolutions. To solve this problem, we proposed warp and refine network (WR-Net). WR-Net is divided into an optical flow warp component and a warp image refinement component. We used the TV-L1 algorithm instead of deep learning-based approaches to extract the optical flow warp component. The deep-learning-based model is trained on the human-centric view of the RGB channel and does not work on geostationary satellites, which is gray-scale one-channel imagery. The refinement network refines the warped image through a multi-temporal fusion layer. We evaluated WR-Net by interpolation of temporal resolution at 4 min intervals to 2 min intervals in large-scale GK2A geostationary meteorological satellite imagery. Furthermore, we applied WR-Net to the future frame prediction task and showed that the explicit use of optical flow can help future frame prediction.

翻译：对地静止卫星影像在气候与天气预报、自然能源规划以及极端天气事件预测中具有重要应用。为实现精确预测，高时空分辨率的对地静止卫星影像至关重要。尽管近期对地静止卫星分辨率有所提升，但由于不同分辨率限制，利用历史至今多颗卫星进行长期气候分析仍存在局限性。针对该问题，我们提出了变形与细化网络（WR-Net）。WR-Net分为光流变形模块和变形图像细化模块。我们采用TV-L1算法替代基于深度学习的方法提取光流变形分量，因为基于深度学习的模型通常在人类视觉中心的RGB通道图像上训练，无法适用于仅含单通道灰度图像的对地静止卫星数据。细化网络通过多时相融合层对变形图像进行优化。我们在大规模GK2A对地静止气象卫星影像上，通过将4分钟间隔的时序分辨率插值至2分钟来评估WR-Net。此外，我们将WR-Net应用于未来帧预测任务，实验表明显式使用光流有助于提升未来帧预测性能。