DSC based Dual-Resunet for radio frequency interference identification

• 作者: Yan-Jun Zhang Yan-Zuo Li Jun Cheng Yi-Hua Yan
• 来源期刊: 天文和天体物理学研究
• 年，卷（期）: 2021,（12）
• 页码范围: 19-29
• 页数: 11页
• 语种: 英文
• DOI: 10.1088/1674-4527/ac2944

摘要

Radio frequency interference(RFI)will pollute the weak astronomical signals received by radio telescopes,which in return will seriously affect the time-domain astronomical observation and research.In this paper,we use a deep learning method to identify RFI in frequency spectrum data,and propose a neural network based on Unet that combines the principles of depthwise separable convolution and residual,named DSC Based Dual-Resunet.Compared with the existing Unet network,DSC Based Dual-Resunet performs better in terms of accuracy,F1 score,and MIoU,and is also better in terms of computation cost where the model size and parameter amount are 12.5％of Unet and the amount of computation is 38％of Unet.The experimental results show that the proposed network is a high-performance and lightweight network,and it is hopeful to be applied to RFI identification of radio telescopes on a large scale.