Assessment of LiDAR-Based Atmospheric Observations Using YOLOv9 for Sky Image Recognition
This study introduces an innovative method to improve the quality and accuracy of LiDAR observation data using YOLOv9 image recognition technology. By correlating daytime cloud image grayscale values and nighttime sky star counts with LiDAR data, this method provides a robust solution for all-weather observations. Experimental results show that this technique can accurately predict LiDAR data quality under various environmental conditions, offering a valuable tool for atmospheric science research and meteorological monitoring.
Authors
- Faquan Li (ORCID: https://orcid.org/0000-0002-5108-0640)
- Chunhai Bai (ORCID: https://orcid.org/0000-0002-0933-6403)
- Jiqin Wang (ORCID: https://orcid.org/0000-0001-9424-613X)
- Yong Yang (ORCID: https://orcid.org/0000-0001-5833-3850)
- Linmei Liu
- Long Xu (ORCID: https://orcid.org/0000-0002-9286-2876)
- Wei Wang (ORCID: https://orcid.org/0000-0002-1227-6202)
- Xin Lin
- Zhaoxiang Lin (ORCID: https://orcid.org/0000-0002-1853-1534)
- Xuewu Cheng
- Xingkui Lin
Institutions
- Minzu University of China (CN)
- Xinjiang Astronomical Observatory (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1109/jsen.2024.3491303
- Citations
- 2
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.41
Funders
- National Natural Science Foundation of China