SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data

	SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data
	Liu, Huiying 1; Guo, Huadong 1; Zhang, Lu 1
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2015
卷号	8 期号:4 页码:252-262
关键词	Classification concentration sea ice support vector machine (SVM) synthetic aperture radar (SAR)
通讯作者	Zhang, L (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China.
英文摘要	An approach to sea ice classification using dual polarization RADARSAT-2 ScanSAR data is presented in this paper. It is based on support vector machine (SVM). In addition to backscatter coefficients and gray-level cooccurrence matrix (GLCM) texture features, sea ice concentration was introduced as a classification basis. To better analyze the backscatter information of sea ice types, we considered two steps that could improve the ScanSAR image quality, the noise floor stripe reduction and the incidence angle normalization. Then, effective GLCM texture characteristics from both polarizations were selected using the proper parameters. The third type of information, sea ice concentration, was extracted from the initial SVM classification result after the optimal SVM model was achieved from the training. The final result was generated by implementing the SVM twice and the decision tree once. Using this method, the classification was improved in two aspects, both of which were related to sea ice concentration. The results showed that the sea ice concentration parameter was effective in dealing with open water and in discriminating pancake ice from old ice. Finally, the maximum likelihood (ML) was run as a comparative test. In conclusion, the sea ice concentration parameter could play a role in SVM classification, and the whole process provided an effective way to classify sea ice using dual polarization ScanSAR data.
研究领域[WOS]	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000358568900022
内容类型	期刊论文
源URL	[http://ir.ceode.ac.cn/handle/183411/38232]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.[Liu, Huiying] Chinese Acad Sci, CAS Key Lab Digital Earth Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China 2.[Liu, Huiying] Univ Chinese Acad Sci, Beijing 100094, Peoples R China 3.[Guo, Huadong 4.Zhang, Lu] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
推荐引用方式 GB/T 7714	Liu, Huiying,Guo, Huadong,Zhang, Lu. SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2015,8(4):252-262.
APA	Liu, Huiying,Guo, Huadong,&Zhang, Lu.(2015).SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,8(4),252-262.
MLA	Liu, Huiying,et al."SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 8.4(2015):252-262.