Sea ice type classification based on random forest machine learning with Cryosat-2 altimeter data | |
Shen, Xiao-yi1; Zhang, Jie2; Meng, Jun-min2; Zhang, Jie; Ke, Chang-qing1; IEEE | |
2017 | |
会议日期 | 5 19, 2017 - 5 21, 2017 |
会议地点 | Shanghai, China |
关键词 | sea ice type classification random forest Cryosat-2 waveform |
DOI | 10.1109/RSIP.2017.7958792 |
英文摘要 | Sea ice type is the most sensitive variables in Arctic ice monitoring and its detailed information is essential for ice situation evaluation, climate prediction and vessels navigating. In this study, we analyzed the different sea ice types with the Cryosat-2 (CS-2) SAR mode waveform data. The waveform of CS-2 data was describe by a set of parameters: pulse peakiness (PP), leading-edge width (LeW), trailing-edge width (TeW), stack standard deviation (SSD) and Maximum value of the echo waveform (Max)] and backscatter coefficient (Sigma0). Random forest (RF) classifier was chosen to classify ice type and the classification results were compared with Arctic and Antarctic Research Institute (AARI) operational ice charts. The results show that 85% of the Arctic surface type can be correctly classified from November 2015 to May 2016, 83% of the FYI can be correctly identified which is the domain ice type in Arctic. In comparison with Bayesian and K nearest-neighbor classifiers, the classification accuracy of RF increased by 5% and 3% respectively. |
会议录 | 2017 INTERNATIONAL WORKSHOP ON REMOTE SENSING WITH INTELLIGENT PROCESSING (RSIP 2017)
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会议录出版者 | IEEE |
会议录出版地 | 345 E 47TH ST, NEW YORK, NY 10017 USA |
语种 | 英语 |
资助项目 | Ministry of Science and Technology of the P. R. China[32292] |
WOS研究方向 | Computer Science ; Remote Sensing |
WOS记录号 | WOS:000414285000002 |
内容类型 | 会议论文 |
源URL | [http://ir.fio.com.cn/handle/2SI8HI0U/4941] ![]() |
专题 | 业务部门_海洋物理与遥感研究室 |
作者单位 | 1.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Collaborat Innovat Ctr South China Sea Studies, Nanjing, Jiangsu, Peoples R China; 2.State Ocean Adm, Inst Oceanog 1, Qingdao, Peoples R China |
推荐引用方式 GB/T 7714 | Shen, Xiao-yi,Zhang, Jie,Meng, Jun-min,et al. Sea ice type classification based on random forest machine learning with Cryosat-2 altimeter data[C]. 见:. Shanghai, China. 5 19, 2017 - 5 21, 2017. |
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