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

doi:10.1109/LGRS.2020.3002848

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion
	Nie, Xiangli1,2 ; Gao, Ruofei 3; Wang, Rui 4; Xiang, Deliang 5
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
	2021-08-01
卷号	18 期号:8 页码:1456-1460
通讯作者	Nie, Xiangli(xiangli.nie@ia.ac.cn)
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.
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
DOI	10.1109/LGRS.2020.3002848
关键词[WOS]	MODEL
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000675210700035
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45499
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
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
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.