  Scientists at the Berkeley SETI Research Center are Searching for
Extraterrestrial Intelligence (SETI) by a new signal detection method that
converts radio signals into spectrograms through Fourier transforms and
classifies signals represented by two-dimensional time-frequency spectrums,
which successfully converts a signal classification problem into an image
classification task. In view of the negative impact of background noises on the
accuracy of spectrograms classification, a new method is introduced in this
paper. After Gaussian convolution smoothing the signals, edge detection
functions are applied to detect the edge of the signals and enhance the outline
of the signals, then the processed spectrograms are used to train the deep
neural network to compare the classification accuracy of various image
classification networks. The results show that the proposed method can
effectively improve the classification accuracy of SETI spectrums.
