Concerning the difficulty in accurately predicting the spatial structure information details in video frame prediction, a method of deep convolutional Long Short Term Memory (LSTM) neural network was proposed by the improvement of the convolutional LSTM neural network. Firstly, the input sequence images were input into the coding network composed of two deep convolutional LSTM of different channels, and the position information change features and the spatial structure information change features of the input sequence images were learned by the coding network. Then, the learned change features were input into the decoding network corresponding to the coding network channel, and the next predicted picture was output by the decoding network. Finally, the picture was input back to the decoding network, and the next picture was predicted, and all the predicted pictures were output after the pre-set loop times. In the experiments on Moving-MNIST dataset, compared with the convolutional LSTM neural network, the proposed method preserved the accuracy of position information prediction, and had stronger spatial structure information detail representation ability with the same training steps. With the convolutional layer of the convolutional Gated Recurrent Unit (GRU) deepened, the method improved the details of the spatial structure information, verifying the versatility of the idea of the proposed method.

To solve the problem that the traditional algorithms, such as Support Vector Machine (SVM) and random forest, cannot make full use of the texture features and optical parameters of satellite images, a method of cloud/snow recognition based on Multi-dimensional multi-grained cascade Forest (M-gcForest) was proposed. Firstly, according to the difference between single-spectral and multi-spectral images, SVM, random forest, Convolution Neural Network (CNN), and gcForest (multi-grained cascade Forest) were selected to recognize cloud and snow on single-spectral satellite images, by quantitatively analyzing the performance of each algorithm on single-spectral images, CNN and M-gcForest were selected for multi-spectral cloud/snow recognition. Finally, improved M-gcForest was used to predict on HJ-1A/1B multi-spectral satellite images. The experimental results show that compared with CNN, the test accuracy of the M-gcForest on the multi-spectral dataset is increased by 0.32%, the training time is reduced by 91.2%, and the testing time is reduced by 53.7%. Therefore, the proposed algorithm has practicability in real-time and accurate snow disaster monitoring tasks.

To solve the problem of gradient vanishing caused by too many layers of Convolutional Neural Network (CNN) in deep reinforcement learning, a deep reinforcement learning method based on weighted densely connected convolutional network was proposed. Firstly, image features were extracted by skip-connection structure in densely connected convolutional network. Secondly, weight coefficients were added into densely connected convolutional neural network, and each layer in a weighted densely connected convolutional network received all the feature maps generated by its previous layers and was initialized the weight in the skip-connection with different value. Finally, the weight of each layer was dynamically adjusted during training to extract features more effectively. Compared with conventional deep reinforcement learning, in GridWorld simulation experiment, the average reward value of the proposed method was increased by 85.67% under the same number of training steps; in FlappyBird simulation experiment, the average reward value was increased by 55.05%. The experimental results show that the proposed method can achieve better performance in game simulation experiments with different difficulty levels.

It has been a hot research area using the complex network theory and method to model the communication network, analyzing the statistical properties in evolving process and guiding the optimization of routing strategies. The research status of the modeling, the analysis of the statistical properties, the optimization of routing strategies and the design of routing protocols in the Internet of Vehicles (IoV) were analyzed. In addition, three improvements were proposed. The first is using the directed weighted graph to describe the topology of IoV. The second is analyzing the key statistical properties influencing the transmission capacity of IoV based on the differences of statistical properties between the IoV and the mobile Ad Hoc network. The third is optimizing the multi-path routing strategies based on Multiple-Input Multiple-Output (MIMO) technologies by the complex network, which means utilizing multiple channels and multiple paths to transmit.