Learning to Generate Radar Image Sequences Using Two-Stage Generative Adversarial Networks
文献类型：期刊论文
| 作者 | Zhang, Chenyang1,2 ; Yang, Xuebing1; Tang, Yongqiang1,2; Zhang, Wensheng1
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2020-03-01 |
| 卷号 | 17期号:3页码:401-405 |
| 关键词 | Deep learning extreme precipitation generative adversarial networks (GANs) radar image sequences |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2019.2922326 |
| 通讯作者 | Zhang, Wensheng(zhangwenshengia@hotmail.com) |
| 英文摘要 | While quantitative precipitation estimation (QPE) using weather radar is widely adopted in operation, precipitation data sets are often highly imbalanced. In particular, extreme precipitation usually lacks representation, which may introduce the bottleneck for radar QPE with machine learning models. Discovering the intrinsic characteristic of extreme precipitation with few samples is challenging. In this letter, we focus on the radar reflectivity data and aim to generate synthetic radar image sequences with respect to extreme precipitation. Considering the relatively long interval between continuous radar images due to radar volume scan, traditional methods in video generation are not suitable. In this letter, we propose Two-stage Generative Adversarial Networks (TsGANs) to address the above-mentioned problem. In general, our TsGAN constructs adversarial process between generators and discriminators: the generator produces samples similar to real data, while the discriminator determines whether or not a sample is eligible. In Stage I, we generate an image sequence containing content and motion features. In Stage II, we design an enhanced net structure to enrich the adversarial processes and further improve the motion features. Experimental testing is performed within the radar coverage in Shenzhen, China, on rainfall events in 2014-2016. Results show that our TsGAN is superior to previous works. |
| 资助项目 | National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61602482] ; National Natural Science Foundation of China[61532006] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000521960200008 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/38731]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 自动化研究所_精密感知与控制研究中心 |
| 通讯作者 | Zhang, Wensheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Chenyang,Yang, Xuebing,Tang, Yongqiang,et al. Learning to Generate Radar Image Sequences Using Two-Stage Generative Adversarial Networks[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2020,17(3):401-405. |
| APA | Zhang, Chenyang,Yang, Xuebing,Tang, Yongqiang,&Zhang, Wensheng.(2020).Learning to Generate Radar Image Sequences Using Two-Stage Generative Adversarial Networks.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,17(3),401-405. |
| MLA | Zhang, Chenyang,et al."Learning to Generate Radar Image Sequences Using Two-Stage Generative Adversarial Networks".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 17.3(2020):401-405. |
入库方式： OAI收割
来源：自动化研究所
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