Semi-supervised Change Detection
Semi-Supervised Change Detection is a change detection methodology that belongs to the class of semi-supervised learning approaches, distinguished by its utilization of a limited set of labeled samples combined with a larger set of unlabeled data to identify spatial changes between multi-temporal remote sensing images.
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3.8K
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252
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99
Institutions
Publications per year
2017–2026
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Haonan Guo, Qian Shi, Andrea Marinoni et al. · Remote Sensing of Environment · 2021 · 179 citations
Efficient handling of concept drift and concept evolution over Stream Data
Ahsanul Haque, Latifur Khan, Michael Baron et al. · 2016 · 118 citations
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Xidian University
Xi'an, China
8 h-index
38 publications
871 citations
Wuhan University
Wuhan, China
8 h-index
22 publications
1.8K citations

University of Trento
Trento, Italy
7 h-index
11 publications
1.9K citations

Chinese Academy of Sciences
Beijing, China
6 h-index
23 publications
670 citations
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
Wuhan, China
4 h-index
11 publications
800 citations

Jadavpur University
Kolkata, India
3 h-index
6 publications
326 citations
Aerospace Information Research Institute
Beijing, China
3 h-index
11 publications
353 citations
Institute of Electronics
Beijing, China
3 h-index
11 publications
289 citations
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18 publications · 1.4K citations
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6 publications · 278 citations
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GRSL · 6 publications · 227 citations
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Repository · 3 publications · 106 citations
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3 publications · 69 citations
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