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An Ensemble of Classifiers Based on Positive and Unlabeled Data in One-Class Remote Sensing Classification

文献类型：期刊论文

作者Liu, Ran; Li, Wenkai; Liu, Xiaoping; Lu, Xingcheng1; Li, Tianhong4; Guo, Qinghua3
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2018
卷号11期号:2页码:572-584
关键词Classifier ensemble one-class classification positive and unlabeled learning (PUL) weighted average weighted vote
ISSN号1939-1404
DOI10.1109/JSTARS.2017.2789213
文献子类Article
英文摘要One-class remote sensing classification refers to the situations when users are only interested in one specific land type without considering other types. The positive and unlabeled learning (PUL) algorithm, which trains a binary classifier from positive and unlabeled data, has been shown to be promising in one-class classification. The implementation of PUL by a single classifier has been investigated. However, implementing PUL using multiple classifiers and creating classifier ensembles based on PUL have not been studied. In this research, we investigate the implementations of PUL using several classifiers, including generalized linear model, generalized additive model, multivariate adaptive regression splines, maximum entropy, backpropagation neural network, and support vector machine, as well as three ensemble methods based on majority vote, weighted average, and weighted vote combination rules. These methods are applied in classifying the urban areas from four remote sensing imagery of different spatial resolutions, including aerial photograph, Landsat 8, WorldView-3, and Gaofen-1. Experimental results show that classifiers can successfully extract the urban areas with high accuracies, and the ensemble methods based on weighted average and weighted vote generally outperform the individual classifiers on different datasets. We conclude that PUL is a promising method in one-class remote sensing classification, and the classifier ensemble based on PUL can significantly improve the accuracy.
学科主题Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
出版地PISCATAWAY
电子版国际标准刊号2151-1535
WOS关键词MAXIMUM-ENTROPY APPROACH ; SUPPORT VECTOR MACHINES ; IMAGE CLASSIFICATION ; NEURAL-NETWORKS ; MODEL ; ACCURACY ; ALGORITHMS ; REGRESSION ; DIVERSITY
语种英语
WOS记录号WOS:000425661700020
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41401516] ; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [15lgpy16]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/20492]  
专题植被与环境变化国家重点实验室
作者单位1.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Labo Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
3.Peking Univ, Coll Environm Sci & Engn, Beijing 100871, Peoples R China
4.Hong Kong Univ Sci & Technol, Div Environm, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Ran,Li, Wenkai,Liu, Xiaoping,et al. An Ensemble of Classifiers Based on Positive and Unlabeled Data in One-Class Remote Sensing Classification[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2018,11(2):572-584.
APA Liu, Ran,Li, Wenkai,Liu, Xiaoping,Lu, Xingcheng,Li, Tianhong,&Guo, Qinghua.(2018).An Ensemble of Classifiers Based on Positive and Unlabeled Data in One-Class Remote Sensing Classification.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,11(2),572-584.
MLA Liu, Ran,et al."An Ensemble of Classifiers Based on Positive and Unlabeled Data in One-Class Remote Sensing Classification".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11.2(2018):572-584.

入库方式： OAI收割

来源：植物研究所

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