Convective Clouds Extraction From Himawari-8 Satellite Images Based on Double-Stream Fully Convolutional Networks

DOI码：10.1109/lgrs.2019.2926402

发表刊物：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

关键字：Convection,Training,Decoding,Convective clouds,deep learning,fully convolutional network (FCN),remote sensing

摘要：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.

合写作者：Tong Wang,Xiaoliang Tan,Kun Zhu

论文类型：期刊论文

通讯作者：Guanzhou Chen

文献类型：J

卷号：17

期号：4

页面范围：553-557

ISSN号：1545-598X

是否译文：否

发表时间：2019-07-19

收录刊物：SCI