OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

doi:10.3390/rs12060987

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification
	Lei, Guangbin 1; Li, Ainong 1; Bian, Jinhu 1; Yan, He 1,2; Zhang, Lulu 1,3; Zhang, Zhengjian 1,4; Nan, Xi 1,3
刊名	REMOTE SENSING
	2020-03-02
卷号	12 期号:6 页码:987
关键词	land cover mapping multiple classifier ensemble (MCE) iterative classification (IC) self-adaptive updating of samples China-Pakistan economic corridor (CPEC) remote sensing
DOI	10.3390/rs12060987
通讯作者	Li, Ainong(ainongli@imde.ac.cn)
产权排序	1
文献子类	Article
英文摘要	Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%-8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.
电子版国际标准刊号	2072-4292
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030303] ; National Natural Science Foundation project of China[41701433] ; National Natural Science Foundation project of China[41631180] ; National Natural Science Foundation project of China[41701432] ; National Natural Science Foundation project of China[41701430] ; National Key Research and Development Program of China[2016YFC0500201-06] ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS[SDS-135-1708] ; CAS Light of West China Program[2015-XBQN-B-24]
WOS关键词	OBJECT-BASED CLASSIFICATION ; ACCURACY ASSESSMENT ; SYSTEM ; INDEX ; INFORMATION ; DESIGN ; FOREST ; SET ; TM
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000526820600089
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation project of China ; National Key Research and Development Program of China ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS ; CAS Light of West China Program
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/34559]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者	Li, Ainong
作者单位	1.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Peoples R China; 2.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Peoples R China; 3.Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Lei, Guangbin,Li, Ainong,Bian, Jinhu,et al. OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification[J]. REMOTE SENSING,2020,12(6):987.
APA	Lei, Guangbin.,Li, Ainong.,Bian, Jinhu.,Yan, He.,Zhang, Lulu.,...&Nan, Xi.(2020).OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification.REMOTE SENSING,12(6),987.
MLA	Lei, Guangbin,et al."OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification".REMOTE SENSING 12.6(2020):987.