关键词: 深度学习, 对称式密集连接卷积神经网络, 图像分割, 地基云图

Abstract: In order to improve the accuracy of ground-based cloud image segmentation, a symmetrical densely connected convolution neural network based cloud image segmentation method is proposed for ground-based cloud image segmentation. The proposed new network structure firstly extracts the features of ground-based cloud images through ordinary convolution layer, then further processes the feature images through continuous dense connection blocks and upper sampling modules, and finally fuses the shallow and deep features of the network in parallel to achieve accurate segmentation of the ground-based cloud images. In the dense block, cross-layer connection is used to transfer the features of the layer used in the network, which makes the features of cloud images reused, and at the same time reduces the problem of gradient disappearance in the training process to a certain extent. The feature maps of shallow and deep networks are connected in parallel to achieve further accurate segmentation of the ground-based cloud images. The experimental results show that this method can improve the segmentation accuracy and has good generalization effect compared with other machine learning methods for ground-based cloud segmentation.

Key words: deep learning, symmetric convolutional neural network with dense connection, segmentation of image, ground-based cloud images