Deep Learning-based Change Detection
Deep Learning-Based Change Detection is a change detection methodology that employs deep neural networks to automatically learn and identify temporal changes from multi-temporal data, distinguishing itself from traditional methods through its capacity for hierarchical feature extraction and end-to-end learning.
590
Publications
45.3K
Citations
1.9K
Authors
536
Institutions
Publications per year
2017–2026
553
Parents
590
Fully Convolutional Siamese Networks for Change Detection
Rodrigo Caye Daudt, B. Le Saux, Alexandre Boulch · 2018 · 1.6K citations
Changedetection.net: A new change detection benchmark dataset
Nil Goyette, Pierre‐Marc Jodoin, Fatih Porikli et al. · 2012 · 814 citations · Full text
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536
Wuhan University
Wuhan, China
49 h-index
239 publications
37.7K citations

Xidian University
Xi'an, China
36 h-index
224 publications
19.3K citations
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
Wuhan, China
36 h-index
107 publications
14.7K citations

Chinese Academy of Sciences
Beijing, China
28 h-index
127 publications
9.9K citations
University of Chinese Academy of Sciences
Beijing, China
18 h-index
48 publications
4.5K citations
Aerospace Information Research Institute
Beijing, China
18 h-index
81 publications
3K citations

University of Trento
Trento, Italy
18 h-index
36 publications
3.2K citations

Nanjing University of Information Science and Technology
Nanjing, China
18 h-index
60 publications
5.9K citations
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119 publications · 11.9K citations
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69 publications · 4.7K citations
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GRSL · 43 publications · 4.6K citations
31
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40 publications · 2.2K citations
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JAG · 21 publications · 1.3K citations
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