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

文献类型：期刊论文

作者Lei, Guangbin1; Li, Ainong1; Bian, Jinhu1; Yan, He1,4; Zhang, Lulu1,2; Zhang, Zhengjian1,3; Nan, Xi1,2
刊名REMOTE SENSING
出版日期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
DOI10.3390/rs12060987
产权排序1
通讯作者Li, Ainong(ainongli@imde.ac.cn)
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
WOS关键词OBJECT-BASED CLASSIFICATION ; ACCURACY ASSESSMENT ; SYSTEM ; INDEX ; INFORMATION ; DESIGN ; FOREST ; SET ; TM
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研究方向Remote Sensing
语种英语
WOS记录号WOS:000526820600089
出版者MDPI
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
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.Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Peoples R China;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, 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.

入库方式： OAI收割

来源：成都山地灾害与环境研究所

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