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AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
Xia, Gui-Song1; Hu, Jingwen1,2; Hu, Fan1,2; Shi, Baoguang3; Bai, Xiang3; Zhong, Yanfei; Zhang, Liangpei1; Lu, Xiaoqiang4; Xia, GS (reprint author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping Rem, Wuhan 430079, Peoples R China.
Department光学影像学习与分析中心
2017-07-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume55Issue:7Pages:3965-3981
Contribution Rank4
AbstractAerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10 000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.
SubtypeArticle
KeywordAerial Images Benchmark Scene Classification
Subject AreaGeochemistry & Geophysics
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2017.2685945
Indexed BySCI
WOS KeywordREMOTE-SENSING IMAGERY ; LATENT DIRICHLET ALLOCATION ; LAND-USE CLASSIFICATION ; VISUAL-WORDS MODEL ; OBJECT DETECTION ; LEARNING ALGORITHMS ; RANDOM-FIELD ; FEATURES ; REPRESENTATION ; BAG
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
Funding OrganizationNational Natural Science Foundation of China(41501462 ; 91338113)
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000404300900027
Citation statistics
Cited Times:316[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/29085
Collection光谱成像技术研究室
Corresponding AuthorXia, GS (reprint author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping Rem, Wuhan 430079, Peoples R China.
Affiliation1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping Rem, Wuhan 430079, Peoples R China
2.Wuhan Univ, Sch Elect Informat, Signal Proc Lab, Wuhan 430072, Peoples R China
3.Huazhong Univ Sci & Technol, Sch Elect Informat, Wuhan 430074, Peoples R China
4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning, Xian 710119, Peoples R China
Recommended Citation
GB/T 7714
Xia, Gui-Song,Hu, Jingwen,Hu, Fan,et al. AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2017,55(7):3965-3981.
APA Xia, Gui-Song.,Hu, Jingwen.,Hu, Fan.,Shi, Baoguang.,Bai, Xiang.,...&Xia, GS .(2017).AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,55(7),3965-3981.
MLA Xia, Gui-Song,et al."AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 55.7(2017):3965-3981.
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