Abstract
Since the CSES (China Seismo-Electromagnetic Satellite) has been in orbit, it has detected a large number of constant-frequency electromagnetic disturbances (CFEDs), which are horizontal lines on the spectrum. In this paper, we present an algorithm for automatic recognition of CFEDs based on computer vision technology. The relevant results are of great significance for analysis of perturbation events and mining of the transformation laws of global space events. First, a grayscale spectrogram is obtained; then, a horizontal convolution kernel is used to enhance the horizontal edge features of the grayscale graph, and finally, black-and-white binarization is performed to complete data preprocessing. The preprocessed data are then fed into an unsupervised cluster model for training and recognition to realize automatic recognition of CFEDs. Experimental results show that the CFED recognition algorithm proposed in this paper is effective, with a recognition accuracy of more than 98%.
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