Standard
A FCN Approach to Blockage Correction in Radars. / Wu, Hao; Liu, Qi; Liu, Xiaodong et al.
Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021. Institute of Electrical and Electronics Engineers Inc., 2022. p. 482-487 (Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Wu, H, Liu, Q, Liu, X, Zhang, Y, Xu, X
& Bilal, M 2022,
A FCN Approach to Blockage Correction in Radars. in
Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021. Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021, Institute of Electrical and Electronics Engineers Inc., pp. 482-487, 19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021, Virtual, Online, Canada,
25/10/21.
https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00086APA
Wu, H., Liu, Q., Liu, X., Zhang, Y., Xu, X.
, & Bilal, M. (2022).
A FCN Approach to Blockage Correction in Radars. In
Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021 (pp. 482-487). (Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00086Vancouver
Wu H, Liu Q, Liu X, Zhang Y, Xu X
, Bilal M.
A FCN Approach to Blockage Correction in Radars. In Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021. Institute of Electrical and Electronics Engineers Inc. 2022. p. 482-487. (Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021). Epub 2021 Oct 28. doi: 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00086
Author
Wu, Hao ; Liu, Qi ; Liu, Xiaodong et al. /
A FCN Approach to Blockage Correction in Radars. Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 482-487 (Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021).
Bibtex
@inproceedings{244dfefce9fd479b943b5e2155105eeb,
title = "A FCN Approach to Blockage Correction in Radars",
abstract = "Doppler weather radar is the most widely used convection detector with the highest resolution in the ground. Echo reflectance data from the weather radar is the key reference for the meteorological department to carry out severe convective weather forecast and early warning, quantitative precipitation estimation(QPE) and quantitative precipitation forecast(QPF). However, in the process of radar detection, it is inevitable to be affected by obstacles, ground object echo interference, radar echo attenuation and other phenomena, resulting in poor data quality of detection results. Therefore, it is very important to correct the missing or disturbed data. On the other hand, with the rapid development of artificial intelligence technology in recent years, more and more meteorological researchers begin to introduce deep learning and other machine learning methods into the research of meteorological field such as weather radar data processing. In this paper, a deep convolutional encoder-decoder network is proposed to correct the beam blocking of weather radar. In this study, the correction of radar beam blockage is regarded as an image inpainting problem. It's the first trying to use deep learning to realize the correction of radar beam blockage. Experiment shows that the method proposed in this paper is significantly better than the traditional method in accuracy, error rate, false alarm rate and other aspects. The method can directly identify and correct the blocking area, and the operation procedure is simple compared traditional methods.",
keywords = "blockage correction, convolutional neural networks, Deep learning, encoder-decoder network, image inpainting, weather radar",
author = "Hao Wu and Qi Liu and Xiaodong Liu and Yonghong Zhang and Xiaolong Xu and Muhammad Bilal",
year = "2022",
month = mar,
day = "15",
doi = "10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00086",
language = "English",
series = "Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "482--487",
booktitle = "Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021",
note = "19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021 ; Conference date: 25-10-2021 Through 28-10-2021",
}
RIS
TY - GEN
T1 - A FCN Approach to Blockage Correction in Radars
AU - Wu, Hao
AU - Liu, Qi
AU - Liu, Xiaodong
AU - Zhang, Yonghong
AU - Xu, Xiaolong
AU - Bilal, Muhammad
PY - 2022/3/15
Y1 - 2022/3/15
N2 - Doppler weather radar is the most widely used convection detector with the highest resolution in the ground. Echo reflectance data from the weather radar is the key reference for the meteorological department to carry out severe convective weather forecast and early warning, quantitative precipitation estimation(QPE) and quantitative precipitation forecast(QPF). However, in the process of radar detection, it is inevitable to be affected by obstacles, ground object echo interference, radar echo attenuation and other phenomena, resulting in poor data quality of detection results. Therefore, it is very important to correct the missing or disturbed data. On the other hand, with the rapid development of artificial intelligence technology in recent years, more and more meteorological researchers begin to introduce deep learning and other machine learning methods into the research of meteorological field such as weather radar data processing. In this paper, a deep convolutional encoder-decoder network is proposed to correct the beam blocking of weather radar. In this study, the correction of radar beam blockage is regarded as an image inpainting problem. It's the first trying to use deep learning to realize the correction of radar beam blockage. Experiment shows that the method proposed in this paper is significantly better than the traditional method in accuracy, error rate, false alarm rate and other aspects. The method can directly identify and correct the blocking area, and the operation procedure is simple compared traditional methods.
AB - Doppler weather radar is the most widely used convection detector with the highest resolution in the ground. Echo reflectance data from the weather radar is the key reference for the meteorological department to carry out severe convective weather forecast and early warning, quantitative precipitation estimation(QPE) and quantitative precipitation forecast(QPF). However, in the process of radar detection, it is inevitable to be affected by obstacles, ground object echo interference, radar echo attenuation and other phenomena, resulting in poor data quality of detection results. Therefore, it is very important to correct the missing or disturbed data. On the other hand, with the rapid development of artificial intelligence technology in recent years, more and more meteorological researchers begin to introduce deep learning and other machine learning methods into the research of meteorological field such as weather radar data processing. In this paper, a deep convolutional encoder-decoder network is proposed to correct the beam blocking of weather radar. In this study, the correction of radar beam blockage is regarded as an image inpainting problem. It's the first trying to use deep learning to realize the correction of radar beam blockage. Experiment shows that the method proposed in this paper is significantly better than the traditional method in accuracy, error rate, false alarm rate and other aspects. The method can directly identify and correct the blocking area, and the operation procedure is simple compared traditional methods.
KW - blockage correction
KW - convolutional neural networks
KW - Deep learning
KW - encoder-decoder network
KW - image inpainting
KW - weather radar
U2 - 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00086
DO - 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00086
M3 - Conference contribution/Paper
AN - SCOPUS:85127569310
T3 - Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
SP - 482
EP - 487
BT - Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
Y2 - 25 October 2021 through 28 October 2021
ER -
