基于对抗和迁移学习的灾害天气卫星云图分类

针对卫星云图中的灾害天气数据存在严重不平衡问题，提出一个结合生成对抗学习（GAN）和迁移学习（TL）的卷积神经网络（CNN）框架以解决上述问题进而提高基于卫星云图的灾害天气分类精度。该框架主要包含基于GAN的数据均衡化模块和基于迁移学习的CNN分类模块。上述2个模块分别从数据和算法层面解决数据的类间不平衡问题，分别得到一个相对均衡的数据集和一个可在不同类别数据上提取相对均衡特征的分类模型，最终实现对卫星云图的分类，提高其中灾害天气的卫星云图类别分类准确率。与此同时所提方法在自建的大规模卫星云图数据上进行了测试，消融性和综合实验结果证明了所提数据均衡方法和迁移学习方法是有效的，且所提框架模型对各个灾害天气类别的分类精度都有显著提升。 

Classification of satellite cloud images of disaster weather based on adversarial and transfer learning

Weather can be forecasted based on clouds. However, how to use deep learning technology to achieve automatic weather forecasting, especially the automatic recognition of disaster weather, is still an unexplored field. Hence, it is necessary to carry out research on the basic problem in the field of automatic identification: the classification of satellite cloud images. Satellite cloud images have serious data imbalance problems. That is, cloud image data related to severe weather accounts for a very small proportion of all cloud image data. Therefore, this paper proposes a framework combining Generative Adversarial Network (GAN) and Transfer Learning (TL) based Convolutional Neural Network (CNN) to solve the problem of low accuracy of disaster weather classification based on satellite cloud images. The framework is mainly divided into a data balancing module based on GAN and a CNN classification module based on transfer learning. The above two modules solve the data imbalance problem from the data and algorithm level respectively, and obtain a relatively balanced dataset and a classification model that can extract relatively balanced features on different types of data. Eventually, the classification of satellite cloud images is achieved and the accuracy of the classification of satellite cloud images in disaster weather is improved. The method proposed in this paper has been tested on self-built large-scale satellite cloud image data. The ablative properties and comprehensive experimental results prove that the proposed data balancing method and transfer learning method are effective, and the proposed framework model has significantly improved the classification accuracy of various disaster weather categories.