Erting Pan

610 total citations · 1 hit paper
20 papers, 463 citations indexed

About

Erting Pan is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Atmospheric Science. According to data from OpenAlex, Erting Pan has authored 20 papers receiving a total of 463 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Media Technology, 12 papers in Computer Vision and Pattern Recognition and 8 papers in Atmospheric Science. Recurrent topics in Erting Pan's work include Advanced Image Fusion Techniques (14 papers), Remote-Sensing Image Classification (14 papers) and Remote Sensing and Land Use (8 papers). Erting Pan is often cited by papers focused on Advanced Image Fusion Techniques (14 papers), Remote-Sensing Image Classification (14 papers) and Remote Sensing and Land Use (8 papers). Erting Pan collaborates with scholars based in China. Erting Pan's co-authors include Xiaoguang Mei, Yong Ma, Jiayi Ma, Fan Fan, Jun Huang, Xiaobing Dai, Hong Zheng, Qinglei Du, Quan‐De Wang and Zhiguang Yang and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Access and Pattern Recognition.

In The Last Decade

Erting Pan

17 papers receiving 454 citations

Hit Papers

Spectral-Spatial Attention Networks for Hyperspectral Ima... 2019 2026 2021 2023 2019 50 100 150 200
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. Spectral-Spatial Attention Networks for Hyperspectral Image Classification
    2019 233 citations Xiaoguang Mei, Erting Pan et al. Remote Sensing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Erting Pan China 9 356 196 158 49 41 20 463
Shuguo Jiang China 9 447 1.3× 195 1.0× 168 1.1× 43 0.9× 110 2.7× 11 570
Xiangpo Wei China 7 360 1.0× 267 1.4× 135 0.9× 56 1.1× 54 1.3× 12 467
Sidrah Shabbir Pakistan 3 282 0.8× 163 0.8× 97 0.6× 39 0.8× 48 1.2× 6 353
Yao Yang China 10 282 0.8× 189 1.0× 100 0.6× 41 0.8× 45 1.1× 15 410
Yishu Peng China 13 386 1.1× 188 1.0× 173 1.1× 40 0.8× 88 2.1× 31 561
Shrutika S. Sawant India 13 330 0.9× 248 1.3× 108 0.7× 58 1.2× 71 1.7× 22 449
Tianyi Jia China 5 239 0.7× 135 0.7× 81 0.5× 76 1.6× 28 0.7× 7 345
Ruoxi Song China 12 443 1.2× 173 0.9× 194 1.2× 50 1.0× 54 1.3× 31 546

Countries citing papers authored by Erting Pan

Since Specialization
Citations

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

Fields of papers citing papers by Erting Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Erting Pan

This figure shows the co-authorship network connecting the top 25 collaborators of Erting Pan. A scholar is included among the top collaborators of Erting Pan 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 Erting Pan. Erting Pan 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.
Zhang, Wenlong, et al.. (2025). Enhancing the Scale Adaptation of Global Trackers for Infrared UAV Tracking. Drones. 9(7). 469–469.
2.
Gao, Song, et al.. (2025). Change-prior guided cross-scale interaction network for remote sensing image change detection. Geo-spatial Information Science. 1–20.
3.
Wang, Kun, Zi Wang, Zhang Li, et al.. (2025). Oriented object detection in optical remote sensing images using deep learning: a survey. Artificial Intelligence Review. 58(11). 2 indexed citations
4.
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
5.
6.
Pan, Erting, et al.. (2024). UnmixDiff: Unmixing-Based Diffusion Model for Hyperspectral Image Synthesis. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–18. 6 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.
Pan, Erting, et al.. (2023). Hyperspectral image destriping and denoising from a task decomposition view. Pattern Recognition. 144. 109832–109832. 5 indexed citations
9.
Pan, Erting, Yong Ma, Xiaoguang Mei, et al.. (2023). Progressive Hyperspectral Image Destriping With an Adaptive Frequencial Focus. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–12. 8 indexed citations
10.
Pan, Erting, et al.. (2023). Hyperspectral Image Denoising Via Spectral Noise Distribution Bootstrap. SSRN Electronic Journal.
11.
Pan, Erting, Yong Ma, Xiaoguang Mei, et al.. (2022). SQAD: Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–14. 35 indexed citations
12.
Pan, Erting, Yong Ma, Xiaoguang Mei, et al.. (2022). D2Net: Deep Denoising Network in Frequency Domain for Hyperspectral Image. IEEE/CAA Journal of Automatica Sinica. 10(3). 813–815. 8 indexed citations
13.
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
14.
Pan, Erting, Yong Ma, Xiaoguang Mei, Fan Fan, & Jiayi Ma. (2021). Unsupervised Stacked Capsule Autoencoder for Hyperspectral Image Classification. 1825–1829. 6 indexed citations
15.
Pan, Erting, Xiaoguang Mei, Quan‐De Wang, Yong Ma, & Jiayi Ma. (2020). Spectral-spatial classification for hyperspectral image based on a single GRU. Neurocomputing. 387. 150–160. 65 indexed citations
16.
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
17.
Mei, Xiaoguang, Erting Pan, Yong Ma, et al.. (2019). Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sensing. 11(8). 963–963. 233 indexed citations breakdown →
18.
Pan, Erting, Yong Ma, Xiaoguang Mei, et al.. (2019). Spectral-Spatial Classification of Hyperspectral Image based on a Joint Attention Network. 413–416. 14 indexed citations
19.
Yang, Zhiguang, et al.. (2019). Multi-Source Medical Image Fusion Based on Wasserstein Generative Adversarial Networks. IEEE Access. 7. 175947–175958. 22 indexed citations
20.
Pan, Erting, Yong Ma, Xiaobing Dai, et al.. (2019). GRU with Spatial Prior for Hyperspectral Image Classification. 7 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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