多维加权密集连接卷积网络的卫星云图云检测

云检测是多光谱卫星云图分析的前提。传统云检测方法不能很好地对多光谱卫星云图进行特征表示，导致了云检测不是很准确。卷积神经网络虽然能有效地提取特征，但训练时会产生梯度扩散，训练效率低，优化困难等问题。针对这些问题，提出多维加权密集连接卷积神经网络模型实现对多光谱卫星云图的云检测。跨层连接能够实现网络中所有层之间的信息流，从而减少训练过程中的梯度消失导致收敛困难的问题。特征图之间连接的权值不同使得网络能够更高效地利用特征信息。通过实验结果对比，该模型可以很好地提取云图特征，提高多光谱云图检测的准确率，具有更好的泛化性能和优化效率。

Satellite image cloud detection using multidimensional weighted densely connected convolutional network

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.