Research on characteristic extraction of vertical ionogram based on deep convolution neural network
This paper proposes a feature extraction method for vertical ionograms using deep convolution neural networks.Based on the labeling of echo information in different layers of the vertical ionogram,a vertical ionogram echo recognition network model including downsampling parts and upsampling parts is constructed to achieve automatic recognition of different echoe information in the vertical ionogram.Using vertical ionograms obtained from experiments,manually label the echo information of different layers of the ionosphere in the vertical ionogram to generate a network model sample dataset.Randomly select 80％of the sample dataset as training data and the remaining data as testing data.After training and testing the network model,the results showed that the network model can automatically and effectively recognize the echo information of different layers in the vertical ionogram.On this basis,combining the corrosion algorithm and connected domain idea in digital image processing,a targeted filter is designed to filter noise,interference,and multi hop echos in the identified echo information,which can effectively extract the characteristic parameters of the test vertical ionogram.And compared with the traditional method,the overall accuracy of feature extraction in this method is better than that of the traditional method,which can provide a new technical means for automatic and accurate feature extraction of the vertical ionogram.
Vertical ionogramDeep convolution neural networkCritical frequency
万方数据
维普
