IGSNRR OpenIR
Urban Change Detection Based on Dempster-Shafer Theory for Multitemporal Very High-Resolution Imagery
Luo, Hui1; Liu, Chong2,3; Wu, Chen4; Guo, Xian5
2018-07-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:7Pages:18
Corresponding AuthorWu, Chen(chen.wu@whu.edu.cn)
AbstractFusing multiple change detection results has great potentials in dealing with the spectral variability in multitemporal very high-resolution (VHR) remote sensing images. However, it is difficult to solve the problem of uncertainty, which mainly includes the inaccuracy of each candidate change map and the conflicts between different results. Dempster-Shafer theory (D-S) is an effective method to model uncertainties and combine multiple evidences. Therefore, in this paper, we proposed an urban change detection method for VHR images by fusing multiple change detection methods with D-S evidence theory. Change vector analysis (CVA), iteratively reweighted multivariate alteration detection (IRMAD), and iterative slow feature analysis (ISFA) were utilized to obtain the candidate change maps. The final change detection result is generated by fusing the three evidences with D-S evidence theory and a segmentation object map. The experiment indicates that the proposed method can obtain the best performance in detection rate, false alarm rate, and comprehensive indicators.
Keywordvery high-resolution image change detection data fusion D-S theory
DOI10.3390/rs10070980
WOS KeywordUNSUPERVISED CHANGE DETECTION ; LAND-COVER CLASSIFICATION ; CHANGE VECTOR ANALYSIS ; REMOTE-SENSING IMAGES ; MAD ; INFORMATION ; ENVIRONMENT ; FRAMEWORK ; DISTANCE ; DESIGN
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61601333] ; National Natural Science Foundation of China[41601453] ; Natural Science Foundation of Jiangxi Province of China[20161BAB213078] ; Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences[2017LDE003] ; Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing[KLIGIP-2017B05]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Jiangxi Province of China ; Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences ; Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000440332500003
PublisherMDPI
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54472
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Chen
Affiliation1.China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China
2.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang 330027, Jiangxi, Peoples R China
3.Jiangxi Normal Univ, Sch Geog & Environm, Nanchang 330027, Jiangxi, Peoples R China
4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Hubei, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Luo, Hui,Liu, Chong,Wu, Chen,et al. Urban Change Detection Based on Dempster-Shafer Theory for Multitemporal Very High-Resolution Imagery[J]. REMOTE SENSING,2018,10(7):18.
APA Luo, Hui,Liu, Chong,Wu, Chen,&Guo, Xian.(2018).Urban Change Detection Based on Dempster-Shafer Theory for Multitemporal Very High-Resolution Imagery.REMOTE SENSING,10(7),18.
MLA Luo, Hui,et al."Urban Change Detection Based on Dempster-Shafer Theory for Multitemporal Very High-Resolution Imagery".REMOTE SENSING 10.7(2018):18.
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