Abstract
The atmospheric ozone layer plays an important role in the interaction of extraterrestrial environmental systems. Obtaining complete ozone data can help relevant scientific researchers better analyze the state of the ozone layer and predict its impact on global or local climate. Due to the operating orbit and other factors, the polar weather satellite may lose some data when collecting data. We use an encoder-decoder convolutional neural network to repair missing data, and use a discriminant network to judge the quality of the data. The model showed amazing performance on a test set that was not related to the training set.
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Funding
This study is supported by the National Key R&D Program of China (Grant No. 2020YFA0608004).
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Feng, L., Huang, F., Zhang, Y., Hu, J. (2022). Deep Learning Feature-Based Method for FY3 Image Inpainting. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_21
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DOI: https://doi.org/10.1007/978-3-031-06794-5_21
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