Abstract
This paper proposes an effective method to improve the spatial resolution of FengYun-2 (FY-2) infrared cloud images via deep convolutional neural networks. The proposed model consists of four parts: shallow feature representation block, stacked multi-scale fusion blocks, global feature fusion block, and feature reconstruction block. The multi-scale fusion block combines dilated convolution, local feature fusion and local residual learning to extract multi-scale local features from the original low-resolution image directly. Then these local features are all merged by the global feature fusion block to reconstruct the residual representations in high-resolution space. For training and testing, we have specially built a dataset of infrared cloud images. We evaluated the proposed method both on simulated and real data. Experimental results demonstrate that the proposed approach achieves improved reconstruction accuracy than the state-of-the-art methods. Besides, the concise structure of the proposed model enables it to be applicable in practice.
Highlights
FengYun-2 (FY-2) series is China’s first-generation geosynchronous orbit satellites, which serves China’s need of weather forecasting, disaster monitoring, and meteorological science research
It is necessary to improve the spatial resolution of infrared cloud images (ICI) for better comprehensively utilizing the useful information of the Visible cloud images (VCI) and the ICI
We propose an end-to-end trainable multi-scale fusion network for infrared cloud image SR (ICI-MSFN)
Summary
FengYun-2 (FY-2) series is China’s first-generation geosynchronous orbit satellites, which serves China’s need of weather forecasting, disaster monitoring, and meteorological science research. The imaging of infrared channel is all-weather, and it can get the temperature information of cloud cluster day and night, whereas the imaging of the visible channel depends on the light condition. Many visible and infrared image fusion methods [1]–[3] and pan-sharpening methods [4], [5] have been proposed, they are not suitable for our task, since visible cloud images are not available at night. Cloud images have richer details, such as edges and textures, than natural images and common infrared images, which means that ICI SR requires a particular model to extract and reconstruct these detail features. We propose an end-to-end trainable multi-scale fusion network for infrared cloud image SR (ICI-MSFN). All available multi-scale features are merged by GFFB to generate the residual representations in HR space. Since the structure of this proposed ICI-MSFN is concise, it can be applied to many meteorological problems in real-time, such as precipitation nowcasting, meteorological element inversion, and typhoon eye positioning
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