Cyclone detection is a classical topic and researchers have developed various methods of cyclone detection based on sea-level pressure, cloud image, wind field, etc. In this article, a deep-learning algorithm is incorporated with modern remote-sensing technology and forms a global-scale cyclone/anticyclone detection model. Instead of using optical images, wind field data obtained from Mean Wind Field-Advanced Scatterometer (MWF-ASCAT) is utilized as the dataset for model training and testing. The wind field vectors are reconstructed and fed to the deep-learning model, which is built based on a faster-region with convolutional neural network (faster-RCNN). The model consists of three modules: a series of convolutional and pooling layers as the feature extractor, a region proposal network that searches for the potential areas of cyclone/anticyclone within the dataset, and the classifier that classifies the proposed region as cyclone or anticyclone through a fully-connected neural network. Compared with existing methods of cyclone detection, the test results indicate that this model based on deep learning is able to reduce the number of false alarms, and at the same time, maintain high accuracy in cyclone detection. An application of this method is presented in the article. By processing temporally continuous data of wind field, the model is able to track the path of Hurricane Irma in September, 2017. The advantages and limitations of the model are also discussed in the article.

中文翻译：

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基于遥感风场和深度学习的全球气旋和反气旋检测模型

旋风检测是一个经典课题，研究人员根据海平面压力，云图，风场等开发了多种旋风检测方法。本文将深度学习算法与现代遥感技术相结合，形成了一种全球规模的气旋/反气旋检测模型。代替使用光学图像，将从平均风场高级散射仪（MWF-ASCAT）获得的风场数据用作模型训练和测试的数据集。重建风场矢量，并将其输入到深度学习模型中，该模型基于带卷积神经网络的快速区域（faster-RCNN）。该模型由三个模块组成：一系列的卷积层和池化层作为特征提取器，区域提议网络，用于搜索数据集中的旋风/反气旋的潜在区域，分类器通过完全连接的神经网络将提议的区域分为旋风或反旋风。与现有的旋风检测方法相比，测试结果表明，该基于深度学习的模型能够减少误报次数，同时保持旋风检测的高精度。本文介绍了此方法的应用。通过处理风场的时间连续数据，该模型能够跟踪2017年9月的飓风“艾尔玛”的路径。本文还讨论了该模型的优缺点。以及通过完全连接的神经网络将拟议区域划分为旋风或反气旋的分类器。与现有的旋风检测方法相比，测试结果表明，该基于深度学习的模型能够减少误报次数，同时保持旋风检测的高精度。本文介绍了此方法的应用。通过处理风场的时间连续数据，该模型能够跟踪2017年9月的飓风“艾尔玛”的路径。本文还讨论了该模型的优缺点。以及通过完全连接的神经网络将拟议区域划分为旋风或反气旋的分类器。与现有的旋风检测方法相比，测试结果表明，该基于深度学习的模型能够减少误报次数，同时保持旋风检测的高精度。本文介绍了此方法的应用。通过处理风场的时间连续数据，该模型能够跟踪2017年9月的飓风“艾尔玛”的路径。本文还讨论了该模型的优缺点。保持旋风检测的高精度。本文介绍了此方法的应用。通过处理风场的时间连续数据，该模型能够跟踪2017年9月的飓风“艾尔玛”的路径。本文还讨论了该模型的优缺点。保持旋风检测的高精度。本文介绍了此方法的应用。通过处理风场的时间连续数据，该模型能够跟踪2017年9月的飓风“艾尔玛”的路径。本文还讨论了该模型的优缺点。