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http://hdl.handle.net/10397/66802
| Title: | Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm | Authors: | Shao, P Shi, WZ He, PF Hao, M Zhang, XK |
Issue Date: | 2016 | Source: | Remote sensing, Mar. 2016, v. 8, no. 3, p. 1-25 | Keywords: | Robust semi-supervised fuzzy C-means Thresholding Remote sensing Clustering with partial supervision Unsupervised change detection Fuzzy C-means |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Remote sensing | EISSN: | 2072-4292 | DOI: | 10.3390/rs8030264 | Rights: | © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). The following publication Shao, P., Shi, W. Z., He, P. F., Hao, M., & Zhang, X. K. (2016). Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sensing, 8(3), (Suppl. ), - is available athttps://dx.doi.org/10.3390/rs8030264 |
| Appears in Collections: | Journal/Magazine Article |
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| Shao_Unsupervised_Robust_Semi-supervised.pdf | 8.7 MB | Adobe PDF | View/Open |
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