Jun Zhou
- Media Technology top 0.02%
- Remote-Sensing Image Classification 152
- Advanced Image Fusion Techniques 77
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- Advanced Image and Video Retrieval Techniques 72
- Video Surveillance and Tracking Methods 41
- Image and Signal Denoising Methods 39
- Advanced Vision and Imaging 34
- Image Retrieval and Classification Techniques 31
- Computational Mathematics top 1%
- Atmospheric Science top 1%
- Remote Sensing and Land Use 57
- Artificial Intelligence top 0.2%
Jun Zhou
542 papers receiving 12.8k citations
Hit Papers
Peers
Comparison fields: 5 of 213
- Media Technology 4.3k
- Computer Vision and Pattern Recognition 4.6k
- Computational Mathematics 98
- Atmospheric Science 1.8k
- Artificial Intelligence 2.7k
Countries citing papers authored by Jun Zhou
This map shows the geographic impact of Jun Zhou'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 Jun Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Zhou more than expected).
Fields of papers citing papers by Jun Zhou
This network shows the impact of papers produced by Jun Zhou. 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 Jun Zhou. The network helps show where Jun Zhou may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun Zhou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 5 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 7 | |
| 5 | 2024 | 13 | |
| 6 | 2024 | 11 | |
| 7 | 2024 | 30 | |
| 8 | 2024 | 10 | |
| 9 | 2024 | 2 | |
| 10 | 2023 | 2 | |
| 11 | 2023 | 8 | |
| 12 | 2023 | 41 | |
| 13 | 2023 | 30 | |
| 14 | 2023 | 0 | |
| 15 | 2022 | 2 | |
| 16 | 2022 | 3 | |
| 17 | 2020 | 0 | |
| 18 | miR-942 promotes proliferation and metastasis of hepatocellular carcinoma cells by inhibiting RRM2B | 2019 | 0 |
| 19 | 2018 | 157 | |
| 20 | 2015 | 24 |
About Jun Zhou
Jun Zhou is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Computational Mathematics, having authored 599 papers that have together received 13.1k indexed citations. Recurring topics across this work include Remote-Sensing Image Classification (152 papers), Advanced Image Fusion Techniques (77 papers), Advanced Image and Video Retrieval Techniques (72 papers), Remote Sensing and Land Use (57 papers), Video Surveillance and Tracking Methods (41 papers), Image and Signal Denoising Methods (39 papers), Advanced Vision and Imaging (34 papers) and Image Retrieval and Classification Techniques (31 papers). The work is most often cited by research in Media Technology (4.3k citations), Computer Vision and Pattern Recognition (4.6k citations) and Computational Mathematics (98 citations). Jun Zhou has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Fan Liu, Zewen Li, Wenjie Yang, Yuntao Qian, Xiao Bai, Fengchao Xiong, Antonio Robles‐Kelly, Sen Jia, Minchao Ye and Jianfeng Lu. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, Pattern Recognition, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Transactions on Image Processing and IEEE Geoscience and Remote Sensing Letters.
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.