Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion

doi:10.1109/LGRS.2020.3002848

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion
	Nie, Xiangli 1,2; Gao, Ruofei 3; Wang, Rui 4; Xiang, Deliang 5
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	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.
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关键词	MODEL
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000675210700035
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
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.