Dou Quan

1.5k citations
49 papers · 859 · h-index 18

Impact in

Papers in

Dou Quan

43 papers receiving 847 citations

Peers

Dou Quan
Comparison fields: 5 of 74
  • Media Technology 296
  • Computer Vision and Pattern Recognition 516
  • Aerospace Engineering 313
  • Atmospheric Science 109
  • Artificial Intelligence 149
Replace Chunlei Huo with:
Chunlei Huo China
Siyuan Hao China
Fukun Bi China
Jianwei Fan China
Ganchao Liu China
Wenhua Zhang China
Yuanxin Ye China
Xiaorun Li China
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Citations per field
00.5×1.6×
Chunlei Huo · 1×
Citations per year

Countries citing papers authored by Dou Quan

Since Specialization
Citations

This map shows the geographic impact of Dou Quan'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 Dou Quan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dou Quan more than expected).

Fields of papers citing papers by Dou Quan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dou Quan. 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 Dou Quan. The network helps show where Dou Quan may publish in the future.

Co-authors

The 25 scholars most cited alongside Dou Quan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dou Quan Line = papers co-authored together Dou Quan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018231
2 202249
3 202243
4 202339
5 201836
6 201935
7 202432
8 202230
9 202326
10 202225
11 202122
12 201822
13 202222
14 202321
15 202320
16 201920
17 202220
18 201617
19 202316
20 202316

About Dou Quan

Dou Quan is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Media Technology, Artificial Intelligence and Atmospheric Science, having authored 49 papers that have together received 859 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (23 papers), Advanced Neural Network Applications (15 papers), Remote-Sensing Image Classification (14 papers), Robotics and Sensor-Based Localization (11 papers), Image Retrieval and Classification Techniques (7 papers), Domain Adaptation and Few-Shot Learning (5 papers), Anomaly Detection Techniques and Applications (4 papers) and Remote Sensing and Land Use (4 papers). The work is most often cited by research in Media Technology (296 citations), Computer Vision and Pattern Recognition (516 citations), Aerospace Engineering (313 citations), Atmospheric Science (109 citations) and Artificial Intelligence (149 citations). Dou Quan has collaborated with scholars based in China, France and Japan. Frequent co-authors include Licheng Jiao, Shuang Wang, Xuefeng Liang, Yanhe Guo, Biao Hou, Ning Huyan, Jocelyn Chanussot, Xiangrong Zhang, Yi Li and Shaowei Wei. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Neural Networks and Learning Systems, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Neurocomputing and IEEE Transactions on Image 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.

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