Minghua Wang

789 total citations · 1 hit paper
32 papers, 538 citations indexed

About

Minghua Wang is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computational Mechanics. According to data from OpenAlex, Minghua Wang has authored 32 papers receiving a total of 538 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 16 papers in Media Technology and 14 papers in Computational Mechanics. Recurrent topics in Minghua Wang's work include Image and Signal Denoising Methods (17 papers), Sparse and Compressive Sensing Techniques (13 papers) and Remote-Sensing Image Classification (12 papers). Minghua Wang is often cited by papers focused on Image and Signal Denoising Methods (17 papers), Sparse and Compressive Sensing Techniques (13 papers) and Remote-Sensing Image Classification (12 papers). Minghua Wang collaborates with scholars based in China, France and Germany. Minghua Wang's co-authors include Jocelyn Chanussot, Qiang Wang, Danfeng Hong, Swalpa Kumar Roy, Lianru Gao, Bing Zhang, Jing Yao, Zhi Li, Jiaxin Li and Zhu Han and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and Pattern Recognition.

In The Last Decade

Minghua Wang

26 papers receiving 525 citations

Hit Papers

Learning Tensor Low-Rank Representation for Hyperspectral... 2022 2026 2023 2024 2022 40 80 120
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.

  1. Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection
    2022 126 citations Minghua Wang, Qiang Wang et al. IEEE Transactions on Cybernetics profile →

Peers

Minghua Wang
Binbin Pan China
Yangmuzi Zhang United States
Gui-Fu Lu China
Guangliang Chen United States
Gui‐Fu Lu China
Kewei Tang China
Meng Ding China
Jing‐Hua Yang China
Anyong Qin China
Binbin Pan China
Minghua Wang
Citations per year, relative to Minghua Wang Minghua Wang (= 1×) peers Binbin Pan

Countries citing papers authored by Minghua Wang

Since Specialization
Citations

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

Fields of papers citing papers by Minghua Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minghua Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Minghua Wang. A scholar is included among the top collaborators of Minghua Wang 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 Minghua Wang. Minghua Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Minghua, et al.. (2025). Enhancing Cloud-Removed Regions in Multispectral Optical Images Using SAR Edge Features. IEEE Geoscience and Remote Sensing Letters. 22. 1–5.
2.
Wang, Minghua, Jing Yao, Bing Zhang, & Jun Zhou. (2025). Adaptive Fusion-Based Two-Flow Spectral–Spatial Transformer Network for SAR-Based Optical Cloud Removal. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 19. 235–248.
3.
Wang, Minghua, et al.. (2025). Cross-satellite hierarchical multimodal denoising for hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 232. 94–108.
4.
Wang, Degang, et al.. (2025). Nonlocal and Deep Priors for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–15. 4 indexed citations
5.
Wang, Degang, et al.. (2025). HADDNLP: Hyperspectral anomaly detection via double nonlocal priors. Pattern Recognition. 172. 112535–112535. 1 indexed citations
6.
Wang, Minghua, et al.. (2024). Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 13966–13980. 20 indexed citations
7.
Gao, Lianru, et al.. (2024). HADGSM: A Unified Nonconvex Framework for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–15. 27 indexed citations
8.
9.
10.
Zhao, Chunhui, Minghua Wang, Nan Su, Yiming Yan, & Shou Feng. (2023). DSTNet: Dynamic-Static Transformer Style Network for Cross-Resolution Vehicle Reidentification. IEEE Geoscience and Remote Sensing Letters. 20. 1–5.
11.
Wang, Minghua, Danfeng Hong, Zhu Han, et al.. (2023). Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A comprehensive review. IEEE Geoscience and Remote Sensing Magazine. 11(1). 26–72. 66 indexed citations
12.
Wang, Minghua, et al.. (2023). Learning Double Subspace Representation for Joint Hyperspectral Anomaly Detection and Noise Removal. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–17. 27 indexed citations
13.
Wang, Minghua, et al.. (2023). A Feature Fusion-Based Transformer Network for Hyperspectral Super-Resolution. 1–5. 1 indexed citations
14.
Wang, Minghua, et al.. (2022). Vision Transformer With Contrastive Learning for Remote Sensing Image Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 738–749. 36 indexed citations
15.
Wang, Minghua, Qiang Wang, Danfeng Hong, Swalpa Kumar Roy, & Jocelyn Chanussot. (2022). Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection. IEEE Transactions on Cybernetics. 53(1). 679–691. 126 indexed citations breakdown →
16.
Wang, Minghua, Qiang Wang, & Jocelyn Chanussot. (2021). Tensor Low-Rank Constraint and $l_0$ Total Variation for Hyperspectral Image Mixed Noise Removal. IEEE Journal of Selected Topics in Signal Processing. 15(3). 718–733. 41 indexed citations
17.
Wang, Minghua, Qiang Wang, Jocelyn Chanussot, & Danfeng Hong. (2021). l₀-l₁ Hybrid Total Variation Regularization and its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing. IEEE Transactions on Geoscience and Remote Sensing. 59(9). 7695–7710. 58 indexed citations
18.
Wang, Minghua, Qiang Wang, Jocelyn Chanussot, & Danfeng Hong. (2021). Total Variation Regularized Weighted Tensor Ring Decomposition for Missing Data Recovery in High-Dimensional Optical Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 12 indexed citations
19.
Wang, Minghua, et al.. (2020). Hyperspectral Image Mixed Noise Removal Based on Multidirectional Low-Rank Modeling and Spatial–Spectral Total Variation. IEEE Transactions on Geoscience and Remote Sensing. 59(1). 488–507. 40 indexed citations
20.
Wang, Minghua, et al.. (2010). Modeling Botnets' Similarity Based on Communication Feature Extraction and IP Assembly. Chinese Journal of Computers. 33(1). 45–54. 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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