Weather phenomenon recognition notably affects many aspects of our daily lives, for example, weather forecast, road condition monitoring, transportation, agriculture, forestry management, and the detection of the natural environment. In contrast, few studies aim to classify actual weather phenomenon images, usually relying on visual observations from humans. To the best of our knowledge, the traditional artificial visual distinction between weather phenomena takes a lot of time and is prone to errors. Although some studies improved the recognition accuracy and efficiency of weather phenomenon by using machine learning, they identified fewer types of weather phenomena. In this paper, a novel deep convolutional neural network (CNN) named MeteCNN is proposed for weather phenomena classification. Meanwhile, we establish a data set called the weather phenomenon database (WEAPD) containing 6,877 images with 11 weather phenomena, which has more categories than the previous data set. The classification accuracy of MeteCNN on the WEAPD testing set is around 92%, and the experimental result demonstrates the superiority and effectiveness of the proposed MeteCNN model. Realizing the automatic and high‐quality classification of weather phenomena images can provide a reference for future research on weather image classification and weather forecasting.

中文翻译：

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基于深度卷积神经网络的图像天气现象分类

天气现象的识别尤其会影响我们日常生活的许多方面，例如天气预报，道路状况监控，运输，农业，林业管理以及自然环境的检测。相反，很少有研究旨在对实际的天气现象图像进行分类，通常依靠人类的视觉观察。据我们所知，天气现象之间的传统人工视觉区分需要大量时间，并且容易出错。尽管一些研究通过使用机器学习提高了天气现象的识别准确性和效率，但他们发现的天气现象类型却更少。本文提出了一种新的名为MeteCNN的深度卷积神经网络（CNN）进行天气现象分类。同时，我们建立了一个名为天气现象数据库（WEAPD）的数据集，其中包含6,877张具有11种天气现象的图像，其类别比以前的数据集更多。MeteCNN在WEAPD测试集上的分类精度约为92％，实验结果证明了所提MeteCNN模型的优越性和有效性。实现天气现象图像的自动高质量分类可以为以后的天气图像分类和天气预报研究提供参考。实验结果证明了该方法的优越性和有效性。实现天气现象图像的自动高质量分类可以为以后的天气图像分类和天气预报研究提供参考。实验结果证明了该方法的优越性和有效性。实现天气现象图像的自动高质量分类可以为以后的天气图像分类和天气预报研究提供参考。