Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion
文献类型：期刊论文
| 作者 | Nie, Xiangli1,2 ; Gao, Ruofei3; Wang, Rui4; Xiang, Deliang5
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| 出版日期 | 2021-08-01 |
| 卷号 | 18期号:8页码:1456-1460 |
| 关键词 | Vegetation Forestry Random forests Feature extraction Remote sensing Data models Training data Deep forest online multiview learning polarimetric synthetic aperture radar (PolSAR) remote sensing data classification |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2020.3002848 |
| 通讯作者 | Nie, Xiangli(xiangli.nie@ia.ac.cn) |
| 英文摘要 | Remote sensing data can be sequentially acquired from different sources or feature spaces, which are regarded as multiple views. For the classification task where the training data arrive in a sequence, online learning (OL) methods are effective by learning new knowledge from incoming samples incrementally. However, it is known that shallow OL models usually have limited performance. In this letter, an online multiview deep forest (OMDF) architecture is proposed, which consists of multiple layers and employs a cascade structure. Each layer is an ensemble of multiple random forests, which process data from different views, respectively. For each view, the outputs of one layer concatenated with the original feature are fed into the next layer. The proposed method learns a deep forest model in an online manner from a stream of multiview data. The structure of every random forest and the weights adjusting the importance among different views will be updated dynamically. Experimental results on multifeature or multifrequency PolSAR data and the fusion of PolSAR and optical data demonstrate that the proposed method can achieve higher test accuracy and significantly improve the performance, especially on small-scale training data, compared with the other methods. |
| WOS关键词 | MODEL |
| 资助项目 | National Natural Science Foundation of China[61602483] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61802408] ; National Natural Science Foundation of China[91948303] ; Fundamental Research Funds for the Central Universities[22120200149] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000675210700035 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45498]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Nie, Xiangli |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 3.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 4.Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China 5.Natl Innovat Inst Technol, Beijing 100091, Peoples R China |
| 推荐引用方式 GB/T 7714 | Nie, Xiangli,Gao, Ruofei,Wang, Rui,et al. Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2021,18(8):1456-1460. |
| APA | Nie, Xiangli,Gao, Ruofei,Wang, Rui,&Xiang, Deliang.(2021).Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,18(8),1456-1460. |
| MLA | Nie, Xiangli,et al."Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18.8(2021):1456-1460. |
入库方式： OAI收割
来源：自动化研究所
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