Online Semisupervised Active Classification for Multiview PolSAR Data

doi:10.1109/TCYB.2020.3026741

	Online Semisupervised Active Classification for Multiview PolSAR Data
	Nie, Xiangli 1,2; Fan, Mingyu 3; Huang, Xiayuan 1,2; Yang, Wenjing 4; Zhang, Bo 5,6,7; Ma, Xiaoshuang 8,9
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2022-06-01
卷号	52 期号:6 页码:4415-4429
关键词	Task analysis Feature extraction Heuristic algorithms Data models Manifolds Semisupervised learning Training Online active learning online multiview learning online semisupervised learning (SSL) polarimetric synthetic aperture radar (PolSAR) data classification
ISSN号	2168-2267
DOI	10.1109/TCYB.2020.3026741
英文摘要	Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and have multiple views obtained from different feature extractors or multiple frequency bands. The fast and accurate classification of PolSAR data in dynamically changing environments is a critical and challenging task. Online learning can handle this task by learning a classifier incrementally from a stream of samples. In this article, we propose an online semisupervised active learning framework for multiview PolSAR data classification, called OSAM. First, a novel online active learning strategy is designed based on the relationships among multiple views and a randomized rule, which allows to only query the labels of some informative incoming samples. Then, in order to utilize both the incoming labeled and unlabeled samples to update the classifiers, a novel online semisupervised learning model is proposed based on co-regularized multiview learning and graph regularization. In addition, the proposed method can deal with the dynamic large-scale multifeature or multifrequency PolSAR data where not only the amount of data but also the number of classes gradually increases in the learning process. Moreover, the mistake bound of the proposed method is derived rigorously. Extensive experiments are conducted on real PolSAR data to evaluate the performance of our algorithm, and the results demonstrate the effectiveness of the proposed method.
资助项目	National Natural Science Foundation of China[62076241] ; National Natural Science Foundation of China[61602483] ; National Natural Science Foundation of China[61772373] ; National Natural Science Foundation of China[61802408] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61933001] ; National Natural Science Foundation of China[91948303] ; Fundamental Research Funds for the Central Universities[22120200149] ; SKLMCCS Open[Y6S9011F4A]
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000819019200037
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/61217]
专题	中国科学院数学与系统科学研究院
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.Chinese Acad Sci, Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 3.Wenzhou Univ, Coll Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China 4.Natl Univ Def Technol, State Key Lab High Performance Comp, Changsha 410073, Peoples R China 5.Chinese Acad Sci, LSEC, AMSS, Beijing 100190, Peoples R China 6.Chinese Acad Sci, AMSS, Inst Appl Math, Beijing 100190, Peoples R China 7.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 8.Anhui Univ, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei 230000, Peoples R China 9.Anhui Univ, Dept Resources & Environm Engn, Hefei 230000, Peoples R China
推荐引用方式 GB/T 7714	Nie, Xiangli,Fan, Mingyu,Huang, Xiayuan,et al. Online Semisupervised Active Classification for Multiview PolSAR Data[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022,52(6):4415-4429.
APA	Nie, Xiangli,Fan, Mingyu,Huang, Xiayuan,Yang, Wenjing,Zhang, Bo,&Ma, Xiaoshuang.(2022).Online Semisupervised Active Classification for Multiview PolSAR Data.IEEE TRANSACTIONS ON CYBERNETICS,52(6),4415-4429.
MLA	Nie, Xiangli,et al."Online Semisupervised Active Classification for Multiview PolSAR Data".IEEE TRANSACTIONS ON CYBERNETICS 52.6(2022):4415-4429.