Knowledge Commons of Institute of Automation,CAS
Classifying Clear Air Echoes via Static and Motion Streams Network | |
Qu, Yuxun1,2; Zhang, Chenyang1; Yang, Xuebing1; Wu, Yajing1; Zhang, Wensheng1,2; Zhang, Guoping3 | |
发表期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
ISSN | 1545-598X |
2022 | |
卷号 | 19页码:5 |
摘要 | Classification of nonprecipitation echoes of radar is an inevitable step in radar-based precipitation estimation. Among nonprecipitation echoes, clear air echoes are specifically difficult to distinguish for their similarity to precipitation echoes. This letter aims to conduct a pixelwise classification of clear air echoes for image sequences of the radar reflectivity. We propose the Static and Motion streams Network (SMNet) to simultaneously utilize the static and motion features. SMNet realizes capturing the spatiotemporal characteristics while maintaining the details of the current frame via a fusion structure and a novel training method. For feature fusion, the static and motion streams are concatenated. Then, for model training, we adopt a dynamic weight assignment strategy to further extract rich information. Finally, we validate our method on an S-band single-polarization radar in Beijing, China, from May to September 2018. The results demonstrate that the overall performance of SMNet is superior to other competitors. |
关键词 | Radar Radar imaging Atmospheric modeling Training Streaming media Image segmentation Image sequences Classification of nonprecipitation echoes clear air echoes feature fusion radar image segmentation |
DOI | 10.1109/LGRS.2021.3097098 |
关键词[WOS] | NONPRECIPITATING ECHOES ; WEATHER RADAR |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018YFB1404400] ; National Natural Science Foundation of China[61906190] ; National Natural Science Foundation of China[41871020] ; National Natural Science Foundation of China[61806202] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000731151800034 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46987 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
通讯作者 | Yang, Xuebing; Zhang, Wensheng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.China Meteorol Adm CMA, Publ Meteorol Serv Ctr, Beijing 100081, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Qu, Yuxun,Zhang, Chenyang,Yang, Xuebing,et al. Classifying Clear Air Echoes via Static and Motion Streams Network[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5. |
APA | Qu, Yuxun,Zhang, Chenyang,Yang, Xuebing,Wu, Yajing,Zhang, Wensheng,&Zhang, Guoping.(2022).Classifying Clear Air Echoes via Static and Motion Streams Network.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5. |
MLA | Qu, Yuxun,et al."Classifying Clear Air Echoes via Static and Motion Streams Network".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5. |
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Classifying_Clear_Ai(2306KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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