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| An Ensemble of Classifiers Based on Positive and Unlabeled Data in One-Class Remote Sensing Classification | |
Liu, Ran; Li, Wenkai; Liu, Xiaoping; Lu, Xingcheng1; Li, Tianhong2; Guo, Qinghua3
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| 2018 | |
| 发表期刊 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| ISSN | 1939-1404 |
| 卷号 | 11期号:2页码:572-584 |
| 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. |
| 关键词 | Classifier ensemble one-class classification positive and unlabeled learning (PUL) weighted average weighted vote |
| 学科领域 | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| DOI | 10.1109/JSTARS.2017.2789213 |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS关键词 | MAXIMUM-ENTROPY APPROACH ; SUPPORT VECTOR MACHINES ; IMAGE CLASSIFICATION ; NEURAL-NETWORKS ; MODEL ; ACCURACY ; ALGORITHMS ; REGRESSION ; DIVERSITY |
| WOS记录号 | WOS:000425661700020 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 文献子类 | Article |
| 出版地 | PISCATAWAY |
| EISSN | 2151-1535 |
| 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] |
| 作者邮箱 | liur49@mail2.sysu.edu.cn ; liwenk3@mail.sysu.edu.cn ; liuxp3@mail.sysu.edu.cn ; xingchenglu2011@gmail.com ; lth@pku.edu.cn ; guo.qinghua@gmail.com |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | 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.Hong Kong Univ Sci & Technol, Div Environm, Hong Kong, Peoples R China 3.Peking Univ, Coll Environm Sci & Engn, Beijing 100871, Peoples R China 4.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, 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. |
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