Auto-extraction of convective clouds is of great significance. Convective clouds often bring heavy rain, strong winds, and other disastrous weather. Early warning of convection can effectively reduce loss. Using remote sensing images, we can get large-scale cloud information, which provides many effective methods for convective clouds detection. In this letter, we proposed a novel method to extract convective clouds. We introduce a novel deep network using only $1 \times 1$ convolution (3ONet) to extract the spectral characteristics. We then combine a 3ONet with the symmetrical dense-shortcut deep fully convolutional networks (SDFCNs) with a double-stream fully convolutional network to extract convective clouds. In the experiment, we used 12 000 Himawari–8 satellite image patches to verify the proposed framework. Experimental results with 0.5882 mean intersection over union (mIOU) pointed out the proposed method can extract convective clouds effectively.

中文翻译：

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基于双流全卷积网络的 Himawari-8 卫星图像对流云提取

对流云的自动提取具有重要意义。对流云经常带来大雨、强风和其他灾难性天气。对流预警可有效减少损失。利用遥感图像可以获得大尺度的云信息，为对流云检测提供了许多有效的方法。在这封信中，我们提出了一种提取对流云的新方法。我们引入了一种新颖的深度网络，仅使用 $1\times 1$ 卷积（3ONet）来提取光谱特征。然后，我们将 3ONet 与对称密集捷径深度全卷积网络 (SDFCN) 与双流全卷积网络相结合，以提取对流云。在实验中，我们使用了 12 000 个 Himawari-8 卫星图像块来验证所提出的框架。实验结果为 0。