关键词: 多维加权密集连接, 卷积神经网络, 多光谱卫星云图, 云检测

Abstract: Cloud detection is the prerequisite for multi-spectral satellite cloud image analysis. The traditional cloud detection method cannot well characterize multispectral satellite cloud images, which leads to a low accuracy in cloud detection. Although deep convolutional neural network can extract features effectively, it will have problems such as gradient diffusion, low training efficiency, hard optimization, and poor generalization. In order to solve these problems, a multidimensional weighted densely connected convolutional neural network is proposed to realize cloud detection of multi-spectral satellite imagery. Cross layer connection can realize the information flow between all layers in the network, thus reducing the difficulty of convergence caused by the disappearance of the gradient in the training process. The different weights of the connection between the feature graphs make the network more efficient use of the feature information. The simulation shows that the proposed model can extract the features of cloud images well and improve the accuracy of multi-spectral cloud image detection, it has better generalization performance and higher optimization efficiency.

Key words: multidimensional weighted densely connected, convolution neural network, multi-spectral satellite cloud image, cloud detection