Teacher, Xi’an University of Posts and Telecommunications China
Yuquan Gan is an Associate Professor at Xi’an University of Posts and Telecommunications, China, specializing in Signal and Information Processing and Hyperspectral Imaging. He holds a Doctorate in Signal and Information Processing from the University of the Chinese Academy of Sciences (2018) and has worked at prestigious institutions like the Xi’an Institute of Optics and Precision Mechanics. His expertise includes hyperspectral image processing, unmixing, and anomaly detection. šš”
Profile
Scopus
š Education
Yuquan Gan completed his Bachelor’s in Software Engineering (2002-2006) from Northwestern Polytechnical University, Xi’an. He earned his Master’s in Signal and Information Processing from the Graduate University of the Chinese Academy of Sciences (2006-2009). His Ph.D. in Signal and Information Processing was awarded by the University of the Chinese Academy of Sciences (2013-2018), focusing on hyperspectral data analysis and processing.
š¼Experience
Yuquan Gan has held multiple academic and research positions, including Assistant Researcher at the Xi’an Institute of Optics and Precision Mechanics (2009-2018) and Associate Researcher (2018-2019). He is now an Associate Professor at Xi’an University of Posts and Telecommunications. Additionally, he was a Visiting Scholar at the University of Wisconsin-Madison (2015-2016). His work focuses on hyperspectral imagery and signal processing.
š Awards & HonorsĀ
Yuquan Gan has received various prestigious awards, including recognition for his Hyperspectral Unmixing Project supported by the Natural Science Foundation of Shaanxi Province (2022). He has contributed extensively to journals such as IEEE Journal of Biomedical and Health Informatics and Scientific Reports ā Nature, earning accolades for his work on hyperspectral anomaly detection and data processing.
š¬ Research Focus
Gan’s primary research focus is on hyperspectral image analysis, including unmixing, classification, and anomaly detection using deep learning and autoencoders. His projects explore techniques to improve the quality of hyperspectral data for applications in remote sensing and biomedical fields. He has received funding from the Natural Science Foundation of Shaanxi for projects on deep learning-based hyperspectral unmixing and spatial-spectral image analysis.
š¹Conclusion
Yuquan Gan has established himself as a leading figure in the field of hyperspectral image processing. His expertise, extensive publication record, and contributions to significant international research make him a highly suitable candidate for the Best Researcher Award. To further solidify his standing, exploring interdisciplinary research applications and expanding his involvement in high-impact industrial collaborations would elevate his work’s societal relevance and visibility.
š Publications
-
Title: Hyperspectral unmixing algorithm based on channel multi-scale dual-stream autoencode
Year: 2025
Authors: Gan, Y., Wang, Y., Yi, C., Wang, Q., & Zhang, J.
Citation: International Journal of Remote Sensing, 1-31. https://doi.org/10.1080/01431161.2025.2463699
-
Title: Local-global feature fusion network for hyperspectral image classification
Year: 2024
Authors: Gan, Y., Zhang, H., Liu, W., Ma, J., Luo, Y., & Pan, Y.
Citation: International Journal of Remote Sensing, 45(22), 8548-8575. https://doi.org/10.1080/01431161.2024.2403622
-
Title: Multiscale Fusion Transformer Network for Hyperspectral Image Classification
Year: 2024
Authors: Gan, Y., Zhang, H., & Yi, C.
Citation: Journal of Beijing Institute of Technology (English Edition), 33(3), 255-270. https://doi.org/10.15918/j.jbit1004-0579.2023.149
-
Title: Joint Processing of Spatial Resolution Enhancement and Spectral Unmixing for Hyperspectral Image
Year: 2022
Authors: Yi, C., Liu, Y., Zheng, L., & Gan, Y.
Citation: IEEE Geoscience and Remote Sensing Letters, 2022(19), 1-5.
-
Title: Anomaly target detection for hyperspectral imagery based on orthogonal feature
Year: 2021
Authors: Gan, Y., Li, L., Liu, Y., et al.
Citation: Journal of Applied Remote Sensing, 15(4), 046501.
-
Title: Endmember extraction from hyperspectral imagery based on QR factorization using Givens rotations
Year: 2019
Authors: Gan, Y., Hu, B., Liu, W., et al.
Citation: IET Image Processing, 13(02), 332-343.
-
Title: Random selection-based adaptive saliency-weighted RXD anomaly detection for hyperspectral imagery
Year: 2018
Authors: Liu, W., Feng, X., Wang, S., Hu, B., Gan, Y., Zhang, X., & Lei, T.
Citation: International Journal of Remote Sensing, 39(8), 2139-2158.
-
Title: A Sparse-Constrained Graph-Regularized Non-negative Matrix Spectral Unmixing Method
Year: 2019
Authors: Gan, Y., Liu, W., Feng, X., et al.
Citation: Spectroscopy and Spectral Analysis, 39(04), 128-137.
-
Title: Dark Channel-based Dehazing of Remote Sensing Images of Natural Disasters
Year: 2015
Authors: Gan, Y., Wen, D., Wang, L., et al.
Citation: Acta Photonica Sinica, 2015(06), 53-57.
-
Title: Global-Multiscale Channel Convolutional Network For Hyperspectral Image Classification
Year: 2024
Authors: Gan, Y., Zhang, H., Yang, Y., & Yi, C.
Citation: 2024 6th International Conference on Natural Language Processing, ICNLP 2024, 226ā230. https://doi.org/10.1109/ICNLP60986.2024.10692766
-
Title: A Spatial-Spectral Joint Auto-encoder Method for Sparse Unmixing of Hyperspectral Images
Year: 2024
Authors: Gan, Y., Wang, Y., Yi, C., et al.
Citation: Proceedings of the 18th National Conference on Signal and Intelligent Information Processing and Applications, 2024. DOI: 10.26914/c.cnkihy.2024.050426