Xiaoguang Mei

5.6k total citations · 4 hit papers
96 papers, 4.1k citations indexed

About

Xiaoguang Mei is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Xiaoguang Mei has authored 96 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Media Technology, 54 papers in Computer Vision and Pattern Recognition and 26 papers in Aerospace Engineering. Recurrent topics in Xiaoguang Mei's work include Advanced Image Fusion Techniques (55 papers), Remote-Sensing Image Classification (51 papers) and Remote Sensing and Land Use (22 papers). Xiaoguang Mei is often cited by papers focused on Advanced Image Fusion Techniques (55 papers), Remote-Sensing Image Classification (51 papers) and Remote Sensing and Land Use (22 papers). Xiaoguang Mei collaborates with scholars based in China, Canada and Australia. Xiaoguang Mei's co-authors include Jiayi Ma, Yong Ma, Jun Huang, Fan Fan, Han Xu, Junjun Jiang, Xiao–Ping Zhang, Linfeng Tang, Chang Li and Jun Chen 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

Xiaoguang Mei

91 papers receiving 4.0k citations

Hit Papers

DDcGAN: A Dual-Discriminator Conditional Generative Adver... 2019 2026 2021 2023 2020 2022 2019 2019 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaoguang Mei China 27 3.3k 2.1k 1.0k 694 309 96 4.1k
Shaohui Mei China 31 2.5k 0.8× 1.9k 0.9× 409 0.4× 986 1.4× 232 0.8× 181 3.8k
Yongqiang Zhao China 34 2.5k 0.8× 2.3k 1.1× 370 0.4× 520 0.7× 592 1.9× 167 3.8k
Shuang Wang China 31 1.1k 0.3× 1.5k 0.7× 1.0k 1.0× 411 0.6× 157 0.5× 196 3.1k
Weiying Xie China 32 2.2k 0.7× 1.1k 0.5× 395 0.4× 775 1.1× 326 1.1× 113 2.9k
Peicheng Zhou China 18 2.1k 0.6× 2.8k 1.3× 697 0.7× 452 0.7× 84 0.3× 24 3.9k
Jie Feng China 30 1.7k 0.5× 974 0.5× 292 0.3× 929 1.3× 249 0.8× 124 2.7k
Xu Tang China 38 2.1k 0.7× 2.2k 1.0× 513 0.5× 836 1.2× 125 0.4× 166 4.0k
Ping Zhong China 24 1.9k 0.6× 1.0k 0.5× 328 0.3× 907 1.3× 154 0.5× 82 2.7k
Chang Li China 19 1.4k 0.4× 939 0.4× 378 0.4× 317 0.5× 151 0.5× 40 1.9k
Xin Wu China 18 1.3k 0.4× 975 0.5× 636 0.6× 491 0.7× 95 0.3× 61 2.4k

Countries citing papers authored by Xiaoguang Mei

Since Specialization
Citations

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

Fields of papers citing papers by Xiaoguang Mei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaoguang Mei

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoguang Mei. A scholar is included among the top collaborators of Xiaoguang Mei 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 Xiaoguang Mei. Xiaoguang Mei 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.
Ma, Yong, et al.. (2025). Latent spectral-spatial diffusion model for single hyperspectral super-resolution. Geo-spatial Information Science. 28(5). 2415–2430.
2.
Ma, Yong, et al.. (2024). IRLF-SRNet: A super-resolution network based on local–global feature enhance-refine for camera-array based infrared light field images. Infrared Physics & Technology. 141. 105494–105494. 1 indexed citations
3.
Yang, Pei, et al.. (2024). Deep blind super-resolution for hyperspectral images. Pattern Recognition. 157. 110916–110916. 3 indexed citations
4.
Lu, Yifan, Jiayi Ma, Xiaoguang Mei, Jun Huang, & Xiao–Ping Zhang. (2024). Feature Matching via Topology-Aware Graph Interaction Model. IEEE/CAA Journal of Automatica Sinica. 11(1). 113–130. 2 indexed citations
5.
Pan, Erting, Yang Yu, Xiaoguang Mei, Jun Huang, & Jiayi Ma. (2024). From the abundance perspective: Multi-modal scene fusion-based hyperspectral image synthesis. Information Fusion. 108. 102419–102419. 6 indexed citations
6.
Ma, Yong, et al.. (2024). UADNet: A Joint Unmixing and Anomaly Detection Network Based on Deep Clustering for Hyperspectral Image. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–19. 15 indexed citations
7.
Pan, Erting, Yong Ma, Xiaoguang Mei, Fan Fan, & Jiayi Ma. (2023). Hyperspectral image denoising via spectral noise distribution bootstrap. Pattern Recognition. 142. 109699–109699. 9 indexed citations
8.
Ma, Yong, et al.. (2023). Two-view correspondence learning using graph neural network with reciprocal neighbor attention. ISPRS Journal of Photogrammetry and Remote Sensing. 202. 114–124. 7 indexed citations
9.
Pan, Erting, et al.. (2023). Hyperspectral image destriping and denoising from a task decomposition view. Pattern Recognition. 144. 109832–109832. 5 indexed citations
10.
Jiang, Junjun, et al.. (2022). Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer. Remote Sensing. 14(19). 4732–4732. 11 indexed citations
11.
Wang, Xinya, et al.. (2022). Group Shuffle and Spectral-Spatial Fusion for Hyperspectral Image Super-Resolution. IEEE Transactions on Computational Imaging. 8. 1223–1236. 17 indexed citations
12.
Pan, Erting, Yong Ma, Fan Fan, Xiaoguang Mei, & Jun Huang. (2021). Hyperspectral Image Classification across Different Datasets: A Generalization to Unseen Categories. Remote Sensing. 13(9). 1672–1672. 19 indexed citations
13.
Ma, Yong, et al.. (2021). Adversarial Autoencoder Network for Hyperspectral Unmixing. IEEE Transactions on Neural Networks and Learning Systems. 34(8). 4555–4569. 75 indexed citations
14.
Ma, Yong, et al.. (2021). TANet: An Unsupervised Two-Stream Autoencoder Network for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–15. 52 indexed citations
15.
Ma, Yong, et al.. (2021). Guided neighborhood affine subspace embedding for feature matching. Pattern Recognition. 124. 108489–108489. 9 indexed citations
16.
Ma, Yong, et al.. (2020). Hyperspectral Anomaly Detection via Integration of Feature Extraction and Background Purification. IEEE Geoscience and Remote Sensing Letters. 18(8). 1436–1440. 53 indexed citations
17.
Ma, Yong, Erting Pan, Fan Fan, et al.. (2019). Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity. Remote Sensing. 11(20). 2434–2434. 16 indexed citations
18.
Ma, Yong, Chang Li, Hao Li, Xiaoguang Mei, & Jiayi Ma. (2018). Hyperspectral Image Classification With Discriminative Kernel Collaborative Representation and Tikhonov Regularization. IEEE Geoscience and Remote Sensing Letters. 15(4). 587–591. 32 indexed citations
19.
Li, Chang, et al.. (2016). Hyperspectral Unmixing with Robust Collaborative Sparse Regression. Remote Sensing. 8(7). 588–588. 31 indexed citations
20.
Zhou, Bo, Shengqing Wang, Yong Ma, et al.. (2013). An infrared image impulse noise suppression algorithm based on fuzzy logic. Infrared Physics & Technology. 60. 346–358. 4 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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