MSLp: Deep Superresolution for Meteorological Satellite Image - Liling Zhao; Hao Yu; Yan Wang (Complexity)
High-resolution meteorological satellite image is the basic data for weather forecasting, climate prediction, and early warning of various meteorological disasters. However, the poor image resolution is limited for both subjective and automated analyses. Through our investigation and study, we found that the meteorological satellite image is a kind of complex data with multimodal and multitemporal characteristics. Fortunately, based on zero-shot learning theory, the complexity of the meteorological satellite image can be used to enhance its own image resolution. In this work, we propose a novel framework called MSLp (Meteorological Satellite Loss phase). Specifically, we choose a zero-shot network as a backbone and propose a phase loss function. A mapping from low- to high-resolution meteorological satellite images was trained for improving the resolution by up to a factor of 4×. Our quantitative study demonstrates the superiority of the proposed approach over ZSSR and bicubic interpolation. For qualitative analysis, visual tests were performed by 7 meteorologists to confirm the utility of the proposed algorithm. The mean opinion score is 9.32 (the full score is 10). These meteorologists think that weather forecasters need higher-resolution meteorological satellite images and the high-resolution images obtained by our method have the potential to be a great help for weather analysis and forecasting.