干旱区生态环境知识资源中心(Arid): Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang

Arid

DOI	10.3724/SP.J.1226.2016.00263
	Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang
	Wang, JunZhan; Qu, JianJun; Zhang, WeiMin; Zhang, KeCun
通讯作者	Wang, JZ
来源期刊	SCIENCES IN COLD AND ARID REGIONS
ISSN	1674-3822
出版年	2016
卷号	8 期号:3 页码:263-267
英文摘要	Classification is an important process in interpretation of synthetic aperture radar (SAR) imagery. As an advanced instrument for remote sensing, the polarimetric SAR has been applied widely in many fields. The main aim of this paper is to explore the ability of the full-polarization SAR data in classification. The study area is a part of Dunhuang, Gansu Province, China. An L-band full-polarization image of Dunhuang which includes quad-polarization modes was acquired by the ALOS-PALSAR (Advanced Land Observing Satellite-the Phased Array type L-band Synthetic Aperture Radar). Firstly, new characteristic information was extracted by the difference operation, ratio operation, and principal component transform based on the full-polarization (HH, HV or VH, VV) modes SAR data. Then the single-, dual-, full-polarization SAR data and new SAR characteristic information were used to analyze quantitatively the classification accuracy based on the Support Vector Machines (SVM). The results show that classification overall accuracy of single-polarization SAR data is poor, and the highest is 56.53% of VV polarization. The classification overall accuracy of dual-polarization SAR is much better than single-polarization, the highest is 74.77% of HV & VV polarization data. The classification overall accuracy of full-polarization SAR is 80.21%, adding the difference characteristic information, ratio characteristic information and the first principal component (PC1) respectively, the overall accuracy increased by 3.09%, 3.38%, 4.14% respectively. When the full-polarization SAR data in combination with the all characteristic information, the classification overall accuracy reached to 91.01%. The full-polarization SAR data in combination with the band math characteristic information or the PC1 can greatly improve classification accuracy.
英文关键词	full-polarization PALSAR classification the Support Vector Machines (SVM)
类型	Article
语种	英语
收录类别	ESCI
WOS记录号	WOS:000391630900009
WOS关键词	POLARIMETRIC SAR IMAGES ; DECOMPOSITION
WOS类目	Geography, Physical
WOS研究方向	Physical Geography
来源机构	中国科学院西北生态环境资源研究院
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/331737
作者单位	[Wang, JunZhan; Qu, JianJun; Zhang, WeiMin; Zhang, KeCun] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Dunhuang Gobi & Desert Ecol & Environm Res Stn, Lanzhou 730000, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Wang, JunZhan,Qu, JianJun,Zhang, WeiMin,et al. Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang[J]. 中国科学院西北生态环境资源研究院,2016,8(3):263-267.
APA	Wang, JunZhan,Qu, JianJun,Zhang, WeiMin,&Zhang, KeCun.(2016).Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang.SCIENCES IN COLD AND ARID REGIONS,8(3),263-267.
MLA	Wang, JunZhan,et al."Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang".SCIENCES IN COLD AND ARID REGIONS 8.3(2016):263-267.