Radio frequency interference detection based on the AC-UNet model

• 作 者: Rui-Qing Yan Cong Dai Wei Liu Ji-Xia Li Si-Ying Chen Xian-Chuan Yu Shi-Fan Zuo Xue-Lei Chen
• 机 构: CollegeofInformationScienceandTechnology,NationalLaboratoryofPatternRecognition,NationalAstronomical
• 出 处: 天文和天体物理学研究:英文版
• 发表日期: 2021年5期
• 关键词: methods:data analysis techniques:image processing methods:observational
• 基金项目: This research was supported by the National Natural Science Foundation of China(Grant Nos.11471045 and 41672323),the Interdiscipline Research Funds of Beijing Normal University。

Radio frequency interference(RFI)is a serious issue in radio astronomy.This paper proposes a U-Net network model with atrous convolution to detect RFI.Using the ability of convolutional neural networks to extract image features of RFI,and learning RFI dis