DOI number:10.1109/lgrs.2019.2926402

Journal:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

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

Abstract: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.

Co-author:Tong Wang,Xiaoliang Tan,Kun Zhu

Indexed by:Journal paper

Correspondence Author:Guanzhou Chen

Document Type:J

Volume:17

Issue:4

Page Number:553-557

ISSN No.:1545-598X

Translation or Not:no

Date of Publication:2019-07-19

Included Journals:SCI