An Automatic Algorithm for Estimating Tropical Cyclone Centers in Synthetic Aperture Radar Imagery
文献类型：期刊论文
| 作者 | Wang, Yan2; Zheng, Gang1,2; Li, Xiaofeng3,6; Zhou, Lizhang2; Liu, Bin2,5; Chen, Peng2; Ren, Lin1,2; Li, Xiaohui4 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2021-08-27 |
| 页码 | 16 |
| 关键词 | Radar polarimetry Heating systems Sea surface Synthetic aperture radar Oceanography Estimation Satellites Sea surface wind direction (SSWD) synthetic aperture radar (SAR) tropical cyclone (TC) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2021.3105705 |
| 通讯作者 | Zheng, Gang(zhenggang@sio.org.cn) ; Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | Synthetic aperture radar (SAR) can monitor the sea surface imprints of tropical cyclones (TCs) with high spatial resolution, day and night. Automatically locating TC center positions in SAR images is a challenging task. This article developed a two-stage, fully automatic TC-center estimation algorithm. First, the sea surface wind directions (SSWDs) at SSWD points are retrieved by the improved local gradient (ILG) method. We incrementally deflected the SSWD outward at a 0.5 degrees angle from -50 degrees to 10 degrees (the negative angles represent clockwise deflection). The heat maps are generated for each of the 121 angles, and the values at each heat map are the cumulative numbers of the lines perpendicular to the compensated SSWDs. The site corresponding to the maximum cumulative number in all 121 heat maps is the coarsely estimated center position. This center search is the culmination if it falls outside the SAR image. Otherwise, the second stage is triggered, and the sub-SAR image (150 km x 150 km) centered at the coarsely estimated center position is extracted. Then, the first-stage procedure is repeated with the sub-SAR image to precisely estimate the center position. Optionally, the precisely estimated center position can be further adjusted by considering that normalized radar cross section (NRCS) is normally minimal at the TC center. We applied the algorithm to 87 SAR images. Five of these images do not contain TC centers. The results are in good agreement with the visually located TC center positions and those in the best track (BT) datasets. |
| 资助项目 | Zhejiang Provincial Natural Science Foundation of China[LR21D060002] ; National Natural Science Foundation of China[41676167] ; Key Research and Development Project of Shandong Province[2019JZZY010102] ; Project of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography[SOEDZZ2003] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000732782500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/177542]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Zheng, Gang; Li, Xiaofeng |
| 作者单位 | 1.Southern Marine Sci & Engn Guangdong Lab, Zhuhai 519082, Peoples R China 2.Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China 3.Chinese Acad Sci, Big Data Ctr, Inst Oceanol, Chinese Acad Sci CAS,Key Lab Ocean Circulat & Wav, Qingdao 266071, Peoples R China 4.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China 5.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China 6.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yan,Zheng, Gang,Li, Xiaofeng,et al. An Automatic Algorithm for Estimating Tropical Cyclone Centers in Synthetic Aperture Radar Imagery[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021:16. |
| APA | Wang, Yan.,Zheng, Gang.,Li, Xiaofeng.,Zhou, Lizhang.,Liu, Bin.,...&Li, Xiaohui.(2021).An Automatic Algorithm for Estimating Tropical Cyclone Centers in Synthetic Aperture Radar Imagery.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,16. |
| MLA | Wang, Yan,et al."An Automatic Algorithm for Estimating Tropical Cyclone Centers in Synthetic Aperture Radar Imagery".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021):16. |
入库方式： OAI收割
来源：海洋研究所
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