Jiang He 何 江

[21] SCI Q1 Top, IF=: L. Li, J. He*, Y. Liang, and Q. Yuan*, “Multi-Scale Dual-Attention SwinUNet for High-Resolution Aboveground Biomass Estimation With Multi-Source Data,” IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), 2026. [Link]

[20] SCI Q1 TOP, IF=: J. He, X.X. Zhu*, “Reverse degradation for remote sensing pan-sharpening,” International Journal of Applied Earth Observation and Geoinformation (JAG), vol. 146, 105085, 2026. [Link] [PDF] [Code]

[19] SCI Q1 TOP, IF=: J. He, L. Lin, Z. Zheng, Q. Yuan*, J. Li, L. Zhang, X.X. Zhu*, “Spatial-X fusion for multi-source satellite imageries,” Remote Sensing of Environment (RSE), vol. 334, 115214, 2026. [Link] [PDF] [Code]

[18] CCF-A: S. Wang, J. He, N. B. Andreo, X.X. Zhu*, “GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution,” Proceedings of the AAAI conference on artificial intelligence (AAAI), 2026. [PDF]

[17] SCI Q1 TOP, IF=: X. Jin, J. He*, Y. Xiao, Z. Lihe, J. Li, and Q. Yuan*, “VCDFormer: Investigating Cloud Detection Approaches in Sub-Second-Level Satellite Videos,” International Journal of Applied Earth Observation and Geoinformation (JAG), vol. 138, 104465, 2025. [Link]

[16] SCI Q1 TOP, IF=: Z. Lihe, Q. Yuan*, J. He*, X. Jin, Y. Xiao, Y. Chen, H. Shen, and L. Zhang, “Ada4DIR: An adaptive model-driven all-in-one image restoration network for remote sensing images,” Information Fusion (INFFUS), vol. 118, 102930, 2025. [Link]

[15] SCI Q1 TOP, IF=: X. Jin, J. He, Y. Xiao, Z. Lihe, X. Liao, J. Li, and Q. Yuan, “RFE-VCR: Reference-enhanced transformer for remote sensing video cloud removal,” ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS), vol. 214, pp. 179-192, 2024. [Link]

[14] SCI Q1 TOP, IF=: Z. Lihe, J. He, Q. Yuan, X. Jin, Y. Xiao, and L. Zhang, “PhDnet: A novel physic-aware dehazing network for remote sensing images,” Information Fusion (INFFUS), vol. 106, 102277, 2024. [Link]

[13] 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]

[12] 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]

[11] 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]

[10] 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]

[9] 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]

[8] 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]

[7] 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]

[6] 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]

[5] 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]

[4] 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]

[3] 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]

[2] 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]

[1] 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]