Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images

doi:10.3390/rs11161927

CORC > 国家天文台 > 中国科学院国家天文台

	Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images
	Wang, Xiaoxue 1; Gao, Xiangwei 1; Zhang, Yuanzhi 2,3; Fei, Xianyun 1; Chen, Zhou 1; Wang, Jian 1; Zhang, Yayi 1; Lu, Xia 1; Zhao, Huimin 1
刊名	REMOTE SENSING
	2019-08-01
卷号	11 期号:16 页码:22
关键词	coastal wetland classification RF algorithm
DOI	10.3390/rs11161927
英文摘要	Wetlands are one of the world's most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang-Worldview-2 and Landsat-8 images-were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are.
资助项目	National Key Research and Development Program of China[2016YFB0501501] ; Natural Science Foundation of China (NSFC)[31270745] ; Natural Science Foundation of China (NSFC)[41506106] ; Lianyungang Land and Resources Project[LYGCHKY201701] ; Lianyungang Science and Technology Bureau Project[SH1629] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program[5508201601] ; Jiangsu Postgraduate Innovation Program[SY201808X]
WOS关键词	VEGETATION CLASSIFICATION ; REMOTE ; WATER ; TM ; MULTIRESOLUTION ; ACCURACY ; FEATURES
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000484387600090
资助机构	National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program
内容类型	期刊论文
源URL	[http://ir.bao.ac.cn/handle/114a11/27647]
专题	中国科学院国家天文台
通讯作者	Gao, Xiangwei
作者单位	1.Jiangsu Ocean Univ, Sch Geomat & Marine Informat, Lianyungang 222002, Peoples R China 2.Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Natl Astron Observ, Key Lab Lunar Sci & Deep Space Explorat, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xiaoxue,Gao, Xiangwei,Zhang, Yuanzhi,et al. Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images[J]. REMOTE SENSING,2019,11(16):22.
APA	Wang, Xiaoxue.,Gao, Xiangwei.,Zhang, Yuanzhi.,Fei, Xianyun.,Chen, Zhou.,...&Zhao, Huimin.(2019).Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images.REMOTE SENSING,11(16),22.
MLA	Wang, Xiaoxue,et al."Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images".REMOTE SENSING 11.16(2019):22.