One-Class Remote Sensing Classification From Positive and Unlabeled Background Data
文献类型：期刊论文
| 作者 | Li, Wenkai; Guo, Qinghua1 ; Elkan, Charles2
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2021 |
| 卷号 | 14页码:730-746 |
| 关键词 | Training Remote sensing Classification algorithms Mathematical model Support vector machines Data models Prediction algorithms Case-control sampling labeled and unlabeled data one-class classification positive and background learning with constraints (PBLC) remote sensing |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2020.3025451 |
| 文献子类 | Article |
| 英文摘要 | One-class classification is a common situation in remote sensing, where researchers aim to extract a single land type from remotely sensed data. Learning a classifier from labeled positive and unlabeled background data, which is the case-control sampling scenario, is efficient for one-class remote sensing classification because labeled negative data are not necessary for model training. In this study, we propose a novel positive and background learning with constraints (PBLC) algorithm to address this one-class classification problem. With user-specified information of maximum probability as the constraint, PBLC infers the posterior probability of positive class directly in one-step model training. We test PBLC on a synthetic dataset and a real aerial photograph to perform different one-class classification tasks. Experimental results demonstrate that PBLC can successfully train linear and nonlinear classifiers including generalized linear model, artificial neural network, and convolutional neural network. Probabilistic and binary predictions by PBLC are more similar to the gold-standard positive-negative method, outperforming the two-step positive and background learning algorithm that post-calibrates a naive classifier based on an estimated constant. Hence, the proposed PBLC algorithm has the potential to solve one-class classification problems in the case-control sampling scenario. |
| 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 | PRESENCE-ONLY DATA ; LOCALITY DESCRIPTIONS ; IMAGE CLASSIFICATION ; SUPPORT ; EXTRACTION ; SVM |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) ; Social Science Citation Index (SSCI) |
| 语种 | 英语 |
| WOS记录号 | WOS:000696430600007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Guangdong Basic and Applied Basic Research Foundation [2020A1515010764] ; National Natural Science Foundation of China [41401516] ; State Key Laboratory of Vegetation and Environmental Change [LVEC-2019kf05] |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/26497]  |
| 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 Lab 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.Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA |
| 推荐引用方式 GB/T 7714 | Li, Wenkai,Guo, Qinghua,Elkan, Charles. One-Class Remote Sensing Classification From Positive and Unlabeled Background Data[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:730-746. |
| APA | Li, Wenkai,Guo, Qinghua,&Elkan, Charles.(2021).One-Class Remote Sensing Classification From Positive and Unlabeled Background Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,730-746. |
| MLA | Li, Wenkai,et al."One-Class Remote Sensing Classification From Positive and Unlabeled Background Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):730-746. |
入库方式： OAI收割
来源：植物研究所
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