Dan Hu
Impact in
- Media Technology top 1%
- Remote-Sensing Image Classification
- Advanced Image Fusion Techniques
- Atmospheric Science top 10%
- Remote Sensing and Land Use
Papers in
-
- Fuzzy Logic and Control Systems 10
- Neural Networks and Applications 9
- Co-authors
- Xianchuan Yu (18 shared papers)Ying Zhan (6 shared papers)Xianchuan Yu (14 shared papers)Jindong Xu (6 shared papers)Zhengwang Wu (15 shared papers)Gang Li (14 shared papers)Weili Lin (14 shared papers)Dinggang Shen (9 shared papers)
- Journals
- Computers & Geosciences (4 papers)IEEE Geoscience and Remote Sensing Letters (3 papers)IEEE Transactions on Medical Imaging (3 papers)Neurocomputing (2 papers)Signal Processing (2 papers)
- Partner nations
- ChinaUnited StatesSouth Korea
In The Last Decade
Dan Hu
85 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 136
- Media Technology 422
- Atmospheric Science 239
- Computer Vision and Pattern Recognition 258
- Cognitive Neuroscience 169
- Signal Processing 83
Countries citing papers authored by Dan Hu
This map shows the geographic impact of Dan Hu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dan Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Hu more than expected).
Fields of papers citing papers by Dan Hu
This network shows the impact of papers produced by Dan Hu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dan Hu. The network helps show where Dan Hu may publish in the future.
Co-authors
The 25 scholars most cited alongside Dan Hu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 95 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 241 | |
| 2 | 2017 | 71 | |
| 3 | 2020 | 56 | |
| 4 | 2016 | 48 | |
| 5 | 2022 | 46 | |
| 6 | 2020 | 38 | |
| 7 | 2020 | 32 | |
| 8 | 2022 | 32 | |
| 9 | Blind Source Separation: Theory and Applications | 2014 | 29 |
| 10 | 2022 | 23 | |
| 11 | 2021 | 23 | |
| 12 | 2015 | 22 | |
| 13 | 2019 | 21 | |
| 14 | 2014 | 21 | |
| 15 | 2012 | 19 | |
| 16 | 2018 | 17 | |
| 17 | 2022 | 16 | |
| 18 | 2023 | 15 | |
| 19 | 2018 | 15 | |
| 20 | 2013 | 15 |
About Dan Hu
Dan Hu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Cognitive Neuroscience and Atmospheric Science, having authored 95 papers that have together received 1.1k indexed citations. Recurring topics across this work include Remote-Sensing Image Classification (14 papers), Remote Sensing and Land Use (13 papers), Functional Brain Connectivity Studies (12 papers), Neonatal and fetal brain pathology (10 papers), Fuzzy Logic and Control Systems (10 papers), Neural Networks and Applications (9 papers), Advanced Image Fusion Techniques (9 papers) and Rough Sets and Fuzzy Logic (8 papers). The work is most often cited by research in Media Technology (422 citations), Atmospheric Science (239 citations), Computer Vision and Pattern Recognition (258 citations), Cognitive Neuroscience (169 citations) and Signal Processing (83 citations). Dan Hu has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Xianchuan Yu, Ying Zhan, Xianchuan Yu, Jindong Xu, Zhengwang Wu, Gang Li, Weili Lin, Dinggang Shen, Li Wang and Han Zhang. Their work appears in journals such as Computers & Geosciences, IEEE Geoscience and Remote Sensing Letters, IEEE Transactions on Medical Imaging, Neurocomputing and Signal Processing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.