Urban Change Detection Based on Dempster-Shafer Theory for Multitemporal Very High-Resolution Imagery
文献类型：期刊论文
| 作者 | Luo, Hui1; Liu, Chong2,3; Wu, Chen4; Guo, Xian5 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2018-07-01 |
| 卷号 | 10期号:7页码:18 |
| 关键词 | very high-resolution image change detection data fusion D-S theory |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs10070980 |
| 通讯作者 | Wu, Chen(chen.wu@whu.edu.cn) |
| 英文摘要 | Fusing 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. |
| WOS关键词 | UNSUPERVISED CHANGE DETECTION ; LAND-COVER CLASSIFICATION ; CHANGE VECTOR ANALYSIS ; REMOTE-SENSING IMAGES ; MAD ; INFORMATION ; ENVIRONMENT ; FRAMEWORK ; DISTANCE ; DESIGN |
| 资助项目 | National 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] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000440332500003 |
| 出版者 | MDPI |
| 资助机构 | National 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 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/54471]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wu, Chen |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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