Jun Lu

878 citations
99 papers · 604 · h-index 13

Impact in

Papers in

Jun Lu

86 papers receiving 565 citations

Peers

Jun Lu
Comparison fields: 5 of 90
  • Media Technology 114
  • Computer Vision and Pattern Recognition 172
  • Atmospheric Science 87
  • Geology 25
  • Artificial Intelligence 141
Replace Stuart Frye with:
Stuart Frye United States
Qi Lv China
Robert F. Kubichek United States
Liang Huang China
Kun Zhu China
A.N. Evans United Kingdom
Falin Wu China
Byungsoo Kim South Korea
Jun Lu relative to Stuart Frye United States Stuart Frye's profile →
Citations per field
00.5×5.6×
Stuart Frye · 1×
Citations per year

Countries citing papers authored by Jun Lu

Since Specialization
Citations

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

Fields of papers citing papers by Jun Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Lu, 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 Jun Lu Line = papers co-authored together Jun Lu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 99 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201883
2 202346
3 202041
4 202135
5 201221
6 201920
7 199520
8 202218
9 201816
10 202315
11 200214
12 202213
13 201812
14 201012
15 202211
16 201310
17 20249
18 20239
19 20249
20 20218

About Jun Lu

Jun Lu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Media Technology and Computational Theory and Mathematics, having authored 99 papers that have together received 604 indexed citations. Recurring topics across this work include Algorithms and Data Compression (14 papers), Remote-Sensing Image Classification (11 papers), Advanced Data Storage Technologies (11 papers), Advanced Neural Network Applications (8 papers), Chaos-based Image/Signal Encryption (8 papers), Advanced Image and Video Retrieval Techniques (8 papers), Remote Sensing and Land Use (7 papers) and Caching and Content Delivery (7 papers). The work is most often cited by research in Media Technology (114 citations), Computer Vision and Pattern Recognition (172 citations), Atmospheric Science (87 citations), Geology (25 citations) and Artificial Intelligence (141 citations). Jun Lu has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Haitao Guo, Marcus Engdahl, Daxin Liu, Lei Xu, Michael Foumelis, Qing Xu, Yves-Louis Desnos, Diego Fernández, José Manuel Delgado Blasco and Lei Ding. Their work appears in journals such as Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Access, Remote Sensing Letters and Journal of King Saud University - Computer and Information Sciences.

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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