Automatic recognition algorithm of lightning whistlers observed by the Search Coil Magnetometer onboard the Zhangheng-1 Satellite

Zhangheng-1 satellite has been recording a large number of electromagnetic fluctuations from the search coil magnetometer (SCM). How to automatically recognize lightning whistlers from the data is important to further explore the temporal and spatial variation of lightning events. Firstly, SCM wave data is processed by Short Time Fourier Transformation (STET) to obtain the Fourier spectrogram. When the lightning whistlers occur, the L-shape could be found in the spectrogram, hence, the spectrogram image was segmented to obtain data group including 316 sub -images with lightning whistlers and 8000 ones without lightning whistlers; secondly, all the sub-images in the data group should be processed by image processing techniques to enhance the lightning whistlers; thirdly, the fuzzy convolution kernel is proposed to process the sub images to filter out the influence of a large number of step edge information. Next, the L-shape convolution kernel is proposed to further enhance the L-shape feature in the image. Finally, the enhanced images as feature vectors are input into the support vector machine (SVM) to train the recognition model. The experimental results show that the proposed automatic lightning whistlers recognition algorithm is effective, and it reaches over 94% both in accuracy, recall rate, Fl value (Fl score), and area under curve (AUC) index.