Sea ice type classification based on random forest machine learning with Cryosat-2 altimeter data

doi:10.1109/RSIP.2017.7958792

CORC > 自然资源部第一海洋研究所 > 自然资源部第一海洋研究所 > 业务部门 > 海洋物理与遥感研究室

	Sea ice type classification based on random forest machine learning with Cryosat-2 altimeter data
	Shen, Xiao-yi 1; Zhang, Jie 2; Meng, Jun-min 2; Zhang, Jie ; Ke, Chang-qing 1; 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)
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.