Multi-temporal Data Fusion
Multi-Temporal Data Fusion is a computational framework that integrates remotely sensed observations acquired at different times to generate a coherent, enhanced representation of Earth surface dynamics, distinguished by its capacity to harmonize temporal, spatial, and spectral variabilities across time-series imagery for improved change detection and monitoring.
453
Publications
28.6K
Citations
1.7K
Authors
605
Institutions
Publications per year
2017–2026
320
453
Zhiqiang Xiao, Shunlin Liang, Jindi Wang et al. · IEEE Transactions on Geoscience and Remote Sensing · 2013 · 771 citations · Full text
GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction
Yuxuan Liang, Songyu Ke, Junbo Zhang et al. · 2018 · 517 citations · Full text
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Chinese Academy of Sciences
Beijing, China
28 h-index
150 publications
7.3K citations
Wuhan University
Wuhan, China
24 h-index
131 publications
8.1K citations
State Key Laboratory of Remote Sensing Science
Beijing, China
24 h-index
137 publications
11.6K citations

Beijing Normal University
Beijing, China
23 h-index
122 publications
15.2K citations
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
Wuhan, China
17 h-index
32 publications
2K citations
Institute of Remote Sensing and Digital Earth
Beijing, China
17 h-index
74 publications
3.7K citations
University of Chinese Academy of Sciences
Beijing, China
16 h-index
42 publications
1.5K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
14 h-index
26 publications
2.8K citations
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69 publications · 4.4K citations
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29 publications · 5.6K citations
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36 publications · 5.1K citations
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JAG · 13 publications · 772 citations
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