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Scientists at the Berkeley SETI Research Center are Searching forExtraterrestrial Intelligence (SETI) by a new signal detection method thatconverts radio signals into spectrograms through Fourier transforms andclassifies signals represented by two-dimensional time-frequency spectrums,which successfully converts a signal classification problem into an imageclassification task. In view of the negative impact of background noises on theaccuracy of spectrograms classification, a new method is introduced in thispaper. After Gaussian convolution smoothing the signals, edge detectionfunctions are applied to detect the edge of the signals and enhance the outlineof the signals, then the processed spectrograms are used to train the deepneural network to compare the classification accuracy of various imageclassification networks. The results show that the proposed method caneffectively improve the classification accuracy of SETI spectrums.

Edge Detection and Deep Learning Based SETI Signal Classification Method