Unsupervised Change Detection
Unsupervised Change Detection is a change detection paradigm that identifies significant alterations between temporal observations in the absence of labeled training data by leveraging intrinsic data structures and statistical disparities to distinguish change from background variation.
349
Publications
23K
Citations
840
Authors
336
Institutions
Publications per year
2017–2026
193
Parents
349
1–5 of 349
840
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336

University of Trento
Trento, Italy
33 h-index
82 publications
11.4K citations

Xidian University
Xi'an, China
28 h-index
147 publications
11.2K citations
Wuhan University
Wuhan, China
20 h-index
77 publications
4.7K citations
Fondazione Bruno Kessler
Trento, Italy
15 h-index
35 publications
2.4K citations

University of Genoa
Genoa, Italy
13 h-index
26 publications
3.8K citations

Chinese Academy of Sciences
Beijing, China
13 h-index
35 publications
1.4K citations
China University of Mining and Technology
Xuzhou, China
12 h-index
34 publications
1.1K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
11 h-index
16 publications
501 citations
1–8 of 336
60 publications · 8K citations
42
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GRSL · 42 publications · 3.7K citations
28
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28 publications · 1.3K citations
22
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22 publications · 923 citations
17
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19 publications · 815 citations
16
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