Ming Wu

2.9k total citations · 2 hit papers
75 papers, 1.8k citations indexed

About

Ming Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Ming Wu has authored 75 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 10 papers in Aerospace Engineering. Recurrent topics in Ming Wu's work include Robotic Path Planning Algorithms (8 papers), Robotics and Sensor-Based Localization (8 papers) and Graph Theory and Algorithms (7 papers). Ming Wu is often cited by papers focused on Robotic Path Planning Algorithms (8 papers), Robotics and Sensor-Based Localization (8 papers) and Graph Theory and Algorithms (7 papers). Ming Wu collaborates with scholars based in China, United States and United Kingdom. Ming Wu's co-authors include Chuang Zhang, Lidong Zhou, Youshan Miao, Jun Guo, Zhanyu Ma, Dongliang Chang, Jiyang Xie, Yi-Zhe Song, Yifeng Ding and Ayan Kumar Bhunia and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Ming Wu

69 papers receiving 1.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

2018 664 citations Chuang Zhang, Ming Wu et al. profile →
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification

2020 270 citations Ming Wu, Jun Guo et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ming Wu China	16	911	550	464	460	367	75	1.8k
Junjue Wang China	19	911 1.0×	164 0.3×	173 0.4×	380 0.8×	795 2.2×	49	2.1k
Xiaowei Shao China	25	587 0.6×	292 0.5×	236 0.5×	257 0.6×	315 0.9×	117	1.9k
Muhammad Shahzad Pakistan	22	546 0.6×	168 0.3×	274 0.6×	353 0.8×	166 0.5×	93	1.8k
Feng Xu China	23	697 0.8×	124 0.2×	133 0.3×	253 0.6×	323 0.9×	121	1.7k
Chuang Zhang China	11	361 0.4×	534 1.0×	459 1.0×	153 0.3×	337 0.9×	64	1.1k
Jesús Manuel de la Cruz García Spain	24	1.2k 1.4×	377 0.7×	141 0.3×	334 0.7×	1000 2.7×	134	2.9k
Zhe Chen China	23	1.2k 1.3×	92 0.2×	176 0.4×	406 0.9×	194 0.5×	95	2.1k
Weipeng Jing China	20	256 0.3×	89 0.2×	154 0.3×	257 0.6×	274 0.7×	121	1.1k
Michael Kampffmeyer Norway	21	789 0.9×	162 0.3×	180 0.4×	641 1.4×	592 1.6×	74	2.0k
Haikel Alhichri Saudi Arabia	24	869 1.0×	113 0.2×	212 0.5×	624 1.4×	815 2.2×	71	2.6k

Countries citing papers authored by Ming Wu

Since Specialization

Citations

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

Fields of papers citing papers by Ming Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Wu. A scholar is included among the top collaborators of Ming Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming Wu. Ming Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Wu, Ming, et al.. (2025). MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting. 12637–12646.

2.

Zhang, Chen, Shijie Cao, Guohao Dai, et al.. (2024). Fine-Grained Structured Sparse Computing for FPGA-Based AI Inference. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(7). 2544–2557.

3.

Li, Bingyao, et al.. (2024). Multi-Scale Receptive Field Rectification of Remote Sensing Images. 9835–9839.

4.

Yang, Cheng, Ke He, Chao Jiang, et al.. (2023). Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey. Energies. 16(14). 5562–5562. 18 indexed citations

5.

Wu, Ming, et al.. (2023). Weakly Supervised Sea Fog Detection in Remote Sensing Images via Prototype Learning. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–13. 2 indexed citations

6.

Wu, Ming, et al.. (2023). SeaMAE: Masked Pre-Training with Meteorological Satellite Imagery for Sea Fog Detection. Remote Sensing. 15(16). 4102–4102. 3 indexed citations

7.

Wu, Ming, et al.. (2023). MoANet: A Motion Attention Network for Sea Fog Detection in Time Series Meteorological Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 1976–1987. 2 indexed citations

8.

Yao, Liangzhong, et al.. (2023). A Comprehensive Evaluation Method of Energy Storage Battery Health Status Based on Combined Weights of Lifetime Characteristics and Operating Characteristics. 14. 922–928.

9.

Wu, Ming, et al.. (2022). The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data. Remote Sensing. 14(17). 4380–4380. 32 indexed citations

10.

Wu, Ming, et al.. (2022). Attention based Long Short-Term Memory Network for Coastal Visibility Forecast. 286. 420–425. 3 indexed citations

11.

Wang, Zhaoqing, Xiangyu Kong, Ming Wu, et al.. (2021). Vecnet: A Spectral and Multi-Scale Spatial Fusion Deep Network for Pixel-Level Cloud Type Classification in Himawari-8 Imagery. 4083–4086. 1 indexed citations

12.

Xiao, Wencong, Jilong Xue, Youshan Miao, et al.. (2020). Distributed Graph Computation Meets Machine Learning. IEEE Transactions on Parallel and Distributed Systems. 31(7). 1588–1604. 4 indexed citations

13.

Han, Wentao, Youshan Miao, Kaiwei Li, et al.. (2014). Chronos. 1–14. 112 indexed citations

14.

Zhang, Chuang, et al.. (2013). Detecting the core network of microblog using snowball sampling. Wireless Personal Multimedia Communications. 1–5. 2 indexed citations

15.

Zhang, Chuang, et al.. (2013). Link prediction of community in Microblog based on Exponential Random Graph Model. Wireless Personal Multimedia Communications. 1–6. 3 indexed citations

16.

Prabhakaran, Vijayan, et al.. (2012). Managing large graphs on multi-cores with graph awareness. USENIX Annual Technical Conference. 4–4. 62 indexed citations

17.

Wu, Ming, et al.. (2009). Cooperative Multi-Robot Monocular-SLAM Using Salient Landmarks. 151–155. 17 indexed citations

18.

Yu, Chunyan, Minghui Wu, & Ming Wu. (2004). Combining rough set theory with neural network theory for pattern recognition. 2. 880–885. 5 indexed citations

19.

Wu, Ming. (2002). Wear Debris Identification Based on Rough Set Artificial Neural Networks. Tribology. 1 indexed citations

20.

Wu, Ming. (1999). Distribution Calculation of Electric Traction Load Harmonics in Three Phase Power System Algorithm, Programming and Example. Journal of the China Railway Society. 2 indexed citations

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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