论文标题
边缘检测和基于深度学习的SETI信号分类方法
Edge Detection and Deep Learning Based SETI Signal Classification Method
论文作者
论文摘要
伯克利SETI研究中心的科学家正在通过一种新的信号检测方法搜索外星智能（SETI），该方法通过傅立叶变换将无线电信号转换为频谱图，并分类由二维时频率谱图所代表的信号，该信号成功地将信号分类问题转换为图像分类任务。鉴于背景噪声对谱图分类的准确性的负面影响，本文引入了一种新方法。高斯卷积平滑信号后，应用边缘检测功能来检测信号的边缘并增强信号的轮廓，然后使用处理后的频谱图来训练深层神经网络以比较各种图像分类网络的分类精度。结果表明，所提出的方法可以有效地提高SETI光谱的分类精度。
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.
