期刊
REMOTE SENSING
卷 10, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/rs10070980
关键词
very high-resolution image; change detection; data fusion; D-S theory
类别
资金
- National Natural Science Foundation of China [61601333, 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]
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.
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