Classification of Solar Radio Spectrum Based on VGG16 Transfer Learning

Abstract

Solar radio bursts are an important part of the study of solar activity, and automatic classification of solar radio spectrum can greatly improve the efficiency of solar activity research. Based on the preprocessing of the original solar radio spectrum images, this paper proposes a solar radio spectrum images classification method based on the VGG16 convolutional neural network and transfer learning. In this method, the pre-trained VGG model is applied to solar radio spectrum recognition. Trained on the generated target data set and adjusted the parameters. The experimental results show that compared with the traditional manual classification method and the existing deep learning classification method, the VGG16 transfer learning classification shows that the TPR of the solar radio burst is better than before. The situation has increased by 12.2%. For the overall classification result analysis, the experimental effect is greatly improved on the basis of the original classification.