Jiang He (何 江)
Postdoctoral Researcher

Chair of Data Science in Earth Observation
Technical University of Munich

Location: Lise-Meitner-Strasse 9 ROOM 221, 85521 Ottobrunn, Germany
Bio | News | Research Interest | Co-authors | Education | Publications | Projects | Services | Awards

Email: hej96.work@gmail.com
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Biography

Jiang He is currently working as a Postdoctoral Researcher in the Chair of Data Science in Earth Observation, Technical University of Munich, advised by Prof. Xiaoxiang Zhu. He received the bachelor's degree from Southwest Jiaotong University, China, and the Doctor of Engineering (Dr.-Ing.) degree in photogrammetry and remote sensing from Wuhan University, China, in 2024, where his research concentrated on "Research on spatial-spectral resolution enhancement of remote sensing images combining data and model-driven algorithms" under the supervision by Prof. Qiangqiang Yuan and Liangpei Zhang.

Jiang's research interests include hyperspectral image processing, deep learning, and data-model-driven algorithms. He has published 22 papers, including 17 SCI papers in INF-FUS, IEEE TNNLS, IEEE GRSM, JAG, IEEE TGRS and other TOP journals, and has presented his work at various international conferences including IGARSS. In addition to research, He also services as the reviewers of nine international journals, including INF-FUS, ISPRS, IEEE TGRS and so on. In 2023, He was invited as a Session Chair for "TH3.R2: Hyperspectral Imaging Denoising and Correction" in IGARSS 2023.

He is now working on hyperspectral image quality improvement, remote sensing sematic extraction, and data-model-driven theory, striving for Earth observation intelligent processing and understanding.


News

Research Interest

 I am working in remote sensing image quality improvement, low-level vision, multi-level version, information fusion and deep learning theory. Currently, I focus on the following research topics:

Co-authors

Education

Publications

Journals:

First-authored:

    [11] SCI Q1 Top, IF=: Y. Li, J. Li, J. He*, X. Liu, and Q. Yuan, “An optimization-driven network with knowledge prior injection for HSI denoising,” IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), vol. 61, pp. 1-17, 2023. [Link] [PDF] [BibTex]

    [10] SCI Q1 Top, IF=: J. He, Q. Yuan, J. Li, Y. Xiao, and L. Zhang, “A self-supervised remote sensing image fusion framework with dual-stage self-learning and spectral super-resolution injection,” ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS), vol. 204, pp. 131-144, 2023. [Link] [PDF] [Code] [BibTex]

    [9] SCI Q1 Top, IF=: J. He, Q. Yuan, J. Li, Y. Xiao, D. Liu, H. Shen, and L. Zhang, “Spectral super-resolution meets deep learning: achievements and challenges,” Information Fusion (INFFUS), vol. 97, pp. 101812, 2023. [Link] [PDF] [Benchmark] [BibTex]

    [8] SCI Q1 Top, IF=: J. He, Q. Yuan, J. Li, and L. Zhang, “PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images,” Information Fusion (INFFUS), vol. 80, pp. 205-225, 2022. [Link] [PDF] [Dataset] [BibTex]

    [7] SCI Q1 TOP, IF=: J. He, Q. Yuan, J. Li, L. Zhang, “A Knowledge Optimization-driven Network with Normalizer-Free Group ResNet Prior for Remote Sensing Image Pan-sharpening,” IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), vol. 60, pp. 1-16, 2022, Art no. 5410716. [Link] [PDF] [BibTex]

    [6] SCI Q1 Top, IF=: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, “Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution,” IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), vol. 33, no. 9, pp. 4213-4227, 2022. [Link] [PDF] [Code] [Dataset] [BibTex]

    [5] SCI Q1 TOP, IF=: J. He, Q. Yuan, J. Li, Y. Xiao, X. Liu, and Y. Zou, “DsTer: A dense spectral transformer for remote sensing spectral super-resolution,” International Journal of Applied Earth Observation and Geoinformation (JAG), vol. 109, pp. 102773, 2022. [Link] [PDF] [BibTex]

    [4] SCI Q1 TOP, IF=, The only-student author, ESI Top 1%: L.-J. Deng, G. Vivone, M. E. Paoletti, G. Scarpa, J. He, Y. Zhang, J. Chanussot, and A. Plaza, “Machine Learning in Pansharpening: A Benchmark, From Shallow to Deep Networks,” IEEE Geoscience and Remote Sensing Magazine (IEEE GRSM), vol. 10, no. 3, pp. 279-315, 2022. [Link] [PDF] [Toolbox] [BibTex]

    [3] SCI Q2, IF=: J. He, J. Li, Q. Yuan, H. Li, and H. Shen, “Spatial-spectral Fusion in Different Swath Widths by a Recurrent Expanding Residual Convolutional Neural Network,” Remote Sensing (RS), vol. 11, no. 19, 2203, 2019. [Link] [PDF] [BibTex]

    [2] SCI Q4, IF=: 何江, 袁强强, 李杰, “面向多光谱卫星成像的广义光谱超分辨率,” 光子学报, vol. 52, no. 2, pp. 0210002, 2023. [Link] [PDF]

    [1] EI, 中国卓越期刊: 张良培, 何江, 杨倩倩, 肖屹, 袁强强, “数据驱动的多源遥感信息融合研究进展,” 测绘学报, vol. 51, no. 7, pp. 1317-1337, 2022. [Link] [PDF] [BibTex]

Co-authored:

    [6] SCI Q1 TOP, IF=: Y. Peng, J. He, Q. Yuan, S. Wang, X. Chu, and L. Zhang, “Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery,” ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS), vol. 202, pp. 303–313, 2023. [Link] [PDF] [BibTex]

    [5] SCI Q1 TOP, IF=, ESI Top 1%: Y. Xiao, Q. Yuan, K. Jiang, J. He, Y. Wang, and L. Zhang, “From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution,” Information Fusion (INFFUS), vol. 96, pp. 297–311, 2023. [Link] [Code] [BibTex]

    [4] SCI Q2, IF=: X. Jin, J. He, Y. Xiao and Q. Yuan, “Learning a Local-Global Alignment Network for Satellite Video Super-Resolution,” IEEE Geoscience and Remote Sensing Letters (IEEE GRSL), vol. 20, pp. 1-5, 2023. [Link] [Bibtex]

    [3] SCI Q1 TOP, IF=: D. Liu, J. Li, Q. Yuan, L. Zheng, J. He, S. Zhao, and Y. Xiao, “An efficient unfolding network with disentangled spatial-spectral representation for hyperspectral image super-resolution,” Information Fusion (INFFUS), vol. 94, pp. 92–111, 2023. [Link] [Code] [BibTex]

    [2] SCI Q1 TOP, IF=, ESI Top 1%: Y. Xiao, Q. Yuan, J. He, Q. Zhang, J. Sun, X. Su, J. Wu, and L. Zhang, “Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer,” International Journal of Applied Earth Observation and Geoinformation (JAG), vol. 108, pp. 102731, 2022. [Link] [Code] [BibTex]

    [1] SCI Q2 TOP, IF=: Y. Xiao, Y. Wang, Q. Yuan, J. He, and L. Zhang, “Generating a long-term (2003−2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS),” Science of The Total Environment (STOTEN), vol. 848, pp. 157747, 2022. [Link] [BibTex]


Conferences:

    [4] S. Wang, Y. Tang, X. Liao, J. He, and etal. , “An Ensemble Learning Approach with Multi-depth Attention Mechanism for Road Damage Detection,” 2022 IEEE International Conference on Big Data (Big Data), pp. 6439-6444, 2022. (CRDDC2022, The 4 th Place) [link] [BibTex]

    [3] B. Arad, R. Timofte, … J. He, and etal. , “NTIRE 2022 Spectral Recovery Challenge and Data Set,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 863-881, 2022. (NTIRE2022, The 8 th Place) [link] [code] [pdf] [BibTex]

    [2] J. Gao, J. Li, Q. Yuan, J. He, and X. Su, “Self-supervised Hyperspectral and Multispectral Image Fusion in Deep Neural Network,” International Conference on Image and Graphics (ICIG), pp. 425-436, 2021.

    [1] J. He, J. Li and Q. Yuan, “Data-Driven and Model-Driven Spectral Superresolution Algorithms: Combination, Analysis and Application for Classification,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Hawaii, USA, pp. 2667-2670, 2020. (EI, Oral) [Slides]


Projects

Teaching

Services

Conferences:



Membership:

  • China Society of Images and Graphics, Student Member, 2021-2024

Journal Reviewer:

  • Information Fusion (INFFUS)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS P&RS)
  • IEEE Transactions on Geoscience and Remote Sensing (TGRS)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS)
  • IEEE Geoscience and Remote Sensing Letters (GRSL)
  • Computer Vision and Image Understanding (CVIU)
  • Computer Methods and Programs in Biomedicine (CMPB)
  • Computational Intelligence and Neuroscience (CIN)
  • IEEE Access
  • Journal of Sensors

Awards
  • 2024, Outstanding Graduate, Wuhan University | 武汉大学优秀毕业生
  • 2022, Special Prize of Wang Zhizhuo Innovative Talent, Wuhan University | 武汉大学“王之卓创新人才”特等奖
  • 2022, The Pacemaker to Outstanding Graduate Student, Wuhan University | 武汉大学优秀研究生标兵
  • 2022, Graduate INNOVA Excellence Prize, Wuhan University | 研究生英诺卓越奖学金
  • 2022, Graduate Academic Innovation Outstanding Prize, Wuhan University | 研究生学术创新校长奖
  • 2020, National Scholarship for Graduate Student, Ministry of Education | 研究生国家奖学金
  • 2020, Outstanding Graduate Student, Wuhan University | 武汉大学优秀研究生
  • 2018, Outstanding Undergraduate, Southwest Jiaotong University | 西南交通大学优秀本科毕业生
  • 2016, Second Prize of The National College Students of Surveying and Mapping Skills Contest, Ministry of Natrual Resources | 全国高等学校大学生测绘技能大赛二等奖