Radio frequency interference detection using efficient multiscale convolutional attention UNet
文献类型：期刊论文
| 作者 | Gu, Fei1; Hao LF(郝龙飞)2 ; Liang, Bo1; Feng, Song1; Wei, Shoulin1; Dai, Wei1; Xu YH(徐永华)2; Li ZX(李志玄)2; Dao, Yihang1
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| 刊名 | Monthly Notices of the Royal Astronomical Society
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| 出版日期 | 2024-04-01 |
| 卷号 | 529期号:4页码:4719-4727 |
| 关键词 | methods: data analysis techniques: image processing |
| ISSN号 | 0035-8711 |
| DOI | 10.1093/mnras/stae868 |
| 产权排序 | 第2完成单位 |
| 文献子类 | Journal article (JA) |
| 英文摘要 | Studying the Universe through radio telescope observation is crucial. However, radio telescopes capture not only signals from the universe but also various interfering signals, known as radio frequency interference (RFI). The presence of RFI can significantly impact data analysis. Ensuring the accuracy, reliability, and scientific integrity of research findings by detecting and mitigating or eliminating RFI in observational data, presents a persistent challenge in radio astronomy. In this study, we proposed a novel deep learning model called EMSCA-UNet for RFI detection. The model employs multiscale convolutional operations to extract RFI features of various scale sizes. Additionally, an attention mechanism is utilized to assign different weights to the extracted RFI feature maps, enabling the model to focus on vital features for RFI detection. We evaluated the performance of the model using real data observed from the 40 m radio telescope at Yunnan Observatory. Furthermore, we compared our results to other models, including U-Net, RFI-Net, and R-Net, using four commonly employed evaluation metrics: precision, recall, F1 score, and IoU. The results demonstrate that our model outperforms the other models on all evaluation metrics, achieving an average improvement of approximately 5 per cent compared to U-Net. Our model not only enhances the accuracy and comprehensiveness of RFI detection but also provides more detailed edge detection while minimizing the loss of useful signals. © 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. |
| 学科主题 | 天文学 |
| URL标识 | 查看原文 |
| 资助项目 | N/A |
| 语种 | 英语 |
| 资助机构 | N/A |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/27151]  |
| 专题 | 云南天文台_射电天文研究组 |
| 作者单位 | 1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China; 2.Yunnan Observatories, Chinese Academy of Science, Yunnan, Kunming, 650000, China |
| 推荐引用方式 GB/T 7714 | Gu, Fei,Hao LF,Liang, Bo,et al. Radio frequency interference detection using efficient multiscale convolutional attention UNet[J]. Monthly Notices of the Royal Astronomical Society,2024,529(4):4719-4727. |
| APA | Gu, Fei.,郝龙飞.,Liang, Bo.,Feng, Song.,Wei, Shoulin.,...&Dao, Yihang.(2024).Radio frequency interference detection using efficient multiscale convolutional attention UNet.Monthly Notices of the Royal Astronomical Society,529(4),4719-4727. |
| MLA | Gu, Fei,et al."Radio frequency interference detection using efficient multiscale convolutional attention UNet".Monthly Notices of the Royal Astronomical Society 529.4(2024):4719-4727. |
入库方式： OAI收割
来源：云南天文台
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