Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification

doi:10.1109/TGRS.2022.3173995

CSpace > 应用数学研究所

	Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification
	Liu, Weiwei 1; Nie, Xiangli 2,3; Zhang, Bo4,5,6 ; Sun, Xian 7,8
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:16
摘要	Image scene classification aiming to assign specific semantic labels for each image is vitally important for the applications of remote sensing (RS) data. In real world, since the observation environment is open and dynamic, RS images are collected sequentially and the numbers of images and classes grow rapidly over time. Most existing scene classification methods are offline learning algorithms, which are inefficient and unscalable for this scenario. In this article, an incremental learning with open-set recognition (ILOSR) framework is proposed for RS image scene classification in the open and dynamic environment, which can identify the unknown classes from a stream of data and learn these new classes incrementally. Specifically, a controllable convex hull-based exemplar selection strategy is designed to address the catastrophic forgetting issue in incremental learning, which can reduce training time and memory footprint effectively. In addition, a new loss function based on prototype learning and uncertainty measurement is proposed for OSR to enhance the interclass discrimination and intraclass compactness of the learned deep features. Experimental results on real RS datasets demonstrate that the proposed method can not only outperform the state-of-the-art approaches on offline classification, incremental learning, and OSR problem separately but also achieve better and more stable performance in the experiments for ILOSR.
关键词	Task analysis Feature extraction Data models Computational modeling Learning systems Training Support vector machines Deep learning incremental learning open-set recognition (OSR) remote sensing (RS) image scene classification
DOI	10.1109/TGRS.2022.3173995
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62076241] ; National Natural Science Foundation of China[91948303] ; National Natural Science Foundation of China[61933001] ; National Natural Science Foundation of China[62171436]
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:000798206000005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/61412
专题	应用数学研究所
通讯作者	Nie, Xiangli
作者单位	1.Univ Sci & Technol Beijing, Sch Mech Engn, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci AMSS, State Key Lab Sci & Engn Comp LSEC, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci AMSS, Inst Appl Math, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 7.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Network Informat Syst Technol NIST, Beijing 100094, Peoples R China 8.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Liu, Weiwei,Nie, Xiangli,Zhang, Bo,et al. Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:16.
APA	Liu, Weiwei,Nie, Xiangli,Zhang, Bo,&Sun, Xian.(2022).Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,16.
MLA	Liu, Weiwei,et al."Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):16.