Qiang Zhang (张 强) |
招收2024级硕士研究生3名,及指导大学生创新训练计划1~2项,欢迎计算机科学与技术、数学、软件工程、人工智能、大数据、智能科学与技术、遥感、地信等专业的同学报名联系!有意愿者请把简历发至本人邮箱,期待你我能共同成长!
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Journals: (* denotes the Corresponding Author)
[22] L. Li, Q. Zhang*, M. Song, and Chein-I Chang, “Feedback band group and variation low rank sparse model for anomaly detection,” submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol.62, pp. 1-19, 2024. (SCI Q1 Top, IF=8.2) [Link] [PDF] [BibTeX]
[21] Q. Zhang, Y. Zheng, Q. Yuan, M. Song, H. Yu, and Y. Xiao, “Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), pp. 1-21, in press, 2023. (SCI Q1 Top, IF=10.4) [Link] [PDF] [Dataset] [BibTeX]
[20] Q. Zhang, J. Zhu, Y. Huang, Q. Yuan, and L. Zhang, “Beyond being wise after the event: Combining spatial, temporal and spectral information for Himawari-8 early-stage wildfire detection,” International Journal of Applied Earth Observation and Geoinformation (JAG), vol. 124, 103506, 2023. (SCI Q1 Top, IF=7.5) [Link] [PDF] [BibTeX]
[19] Q. Zhang, Y. Dong, Q. Yuan, M. Song, and H. Yu, “Combined deep priors with low-rank tensor factorization for hyperspectral image restoration,” IEEE Geoscience and Remote Sensing Letters (GRSL), vol. 20, pp. 1-5, 2023. (SCI Q2, IF=4.8) [Link] [PDF] [Dataset] [BibTeX]
[18] Q. Zhang, Q. Yuan, M. Song, H. Yu, and L. Zhang, “Cooperated spectral low-rankness prior and deep spatial prior for HSI unsupervised denoising,” IEEE Transactions on Image Processing (TIP), vol. 31, pp. 6356-6368, 2022. (CCF-A, SCI Q1 Top, IF=10.6) [News] [Link] [PDF] [Dataset] [BibTeX]
[17] Q. Zhang, Q. Yuan, T. Jin, M. Song, and F. Sun, “SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022,” Earth System Science Data (ESSD), vol. 14, pp. 4473–4488, 2022. (SCI Q1 Top, IF=11.4) [Link] [PDF] [Dataset] [Code] [BibTeX]
[16] Q. Zhang, Q. Yuan, J. Li, Y. Wang, F. Sun, and L. Zhang, “Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013-2019,” Earth System Science Data (ESSD), vol. 13, pp. 1385-1401, 2021. (SCI Q1 Top, IF=11.4) [Link] [PDF] [Project] [Dataset] [Code] [BibTeX]
[15] Q. Zhang, Q. Yuan, Z. Li, F. Sun, and L. Zhang, “Combined deep prior with low-rank tensor SVD for thick cloud removal in multitemporal images,” ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS P&RS), vol. 177, pp. 161-173, 2021. (SCI Q1 Top, IF=12.7) [Link] [PDF] [Dataset] [BibTeX]
[14] Q. Zhang, Q. Yuan, J. Li, F. Sun, and L. Zhang, “Deep spatio-spectral Bayesian posterior for hyperspectral image non-i.i.d. noise removal,” ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS P&RS), vol. 164, pp. 125-137, 2020. (SCI Q1 Top, IF=12.7) [Link] [PDF] [Dataset] [BibTeX]
[13] Q. Zhang, Q. Yuan, J. Li, Z. Li, H. Shen, and L. Zhang, “Thick cloud and cloud shadow removal in multitemporal images using progressively spatio-temporal patch group deep learning,” ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS P&RS), vol. 162, pp. 148-160, 2020. (SCI Q1 Top, IF=12.7) [Link] [PDF] [Code] [Dataset] [BibTeX] [Citations: 100+]
[12] Q. Zhang, Q. Yuan, J. Li, X. Liu, H. Shen, and L. Zhang, “Hybrid noise removal in hyperspectral imagery with spatial-spectral gradient network,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 57, no. 10, pp. 7317-7329, 2019. (SCI Q1 Top, IF=8.2) [Link] [PDF] [Dataset] [BibTeX] [Citations: 100+]
[11] Q. Zhang, Q. Yuan, C. Zeng, X. Li, and Y. Wei, “Missing data reconstruction in remote sensing image with a unified spatial-temporal-spectral deep convolutional neural network,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 56, no. 8, pp. 4274-4288, 2018. (SCI Q1 Top, IF=8.2, ESI Highly Cited Paper) [Link] [PDF] [Code] [BibTeX] [Citations: 300+]
[10] Q. Zhang, Q. Yuan, J. Li, Z. Yang, and X. Ma, “Learning a dilated residual network for SAR image despeckling,” Remote Sensing (RS), vol. 10, no. 2, 196, 2018. (SCI Q2, IF=5) [Link] [PDF] [BibTeX] [Citations: 100+]
[09] Q. Yuan, Q. Zhang, J. Li, H. Shen, and L. Zhang, “Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 57, no. 2, pp. 1205-1218, 2019. (SCI Q1 Top, IF=8.2, ESI Highly Cited Paper) [Link] [PDF] [Code] [Dataset] [BibTeX] [Citations: 300+]
[08] L. Li, M. Song*, Q. Zhang*, and Y. Dong, “Hyperspectral denoising via global variation and local structure low-rank model,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 61, pp. 1-14, 2023. (SCI Q1 Top, IF=8.2) [Link] [PDF] [BibTeX]
[07] L. Li, M. Song, Q. Zhang, Y. Dong, Y. Wang, and Q. Yuan, “Local extremum constrained total variation model for natural and hyperspectral image non-blind deblurring,” IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), in press, 2024. (SCI Q1 Top, IF=8.4) [Link] [PDF] [BibTeX]
[06] W. Zhang, Z. li, G. Li, P. Zhuang, G. Hou, Q. Zhang, and C. Li, “GACNet: Generate adversarial-driven cross-aware network for hyperspectral wheat variety identification,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 62, pp. 1-15, 2024. (SCI Q1 Top, IF=8.2) [Link] [PDF] [BibTeX]
[05] Y. Xiao, Q. Yuan, Q. Zhang, and L. Zhang, “Deep blind super-resolution for satellite video,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 61, pp. 1-16, 2023. (SCI Q1 Top, IF=8.2) [Link] [PDF] [Code] [BibTeX]
[04] H. Yang, H. Yu, K. Zheng, J. Hu, T. Tao, and Q. Zhang, “Hyperspectral image classification based on interactive transformer and CNN with multilevel feature fusion network,” IEEE Geoscience and Remote Sensing Letters (GRSL), vol. 20, pp. 1-5, 2023. (SCI Q2, IF=4.8) [Link] [PDF] [BibTeX]
[03] 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, 102731, 2022. (SCI Q1 Top, IF=7.5, ESI Highly Cited Paper) [Link] [PDF] [Code] [BibTeX]
[02] W. Zhang, Z. Li, H. Sun, Q. Zhang, P. Zhuang, and C. Li, “SSTNet: Spatial, spectral, and texture aware attention network using hyperspectral image for corn variety identification,” IEEE Geoscience and Remote Sensing Letters (GRSL), vol. 19, pp. 1-5, 2022. (SCI Q2, IF=4.8) [Link] [PDF] [BibTeX]
[01] J. Lin, T. Huang, X. Zhao, Y. Chen, Q. Zhang, and Q. Yuan, “Robust thick cloud removal for multi-temporal remote sensing images using coupled tensor factorization,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 60, pp. 1-16, 2022. (SCI Q1 Top, IF=8.2) [Link] [PDF] [BibTeX]
Conferences:
[09] J. Zhu, Q. Zhang*, and Y. Zheng, “Forest early wildfire detection via multi-source and multi-type information fusion,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Athens, Greece, 2024. (EI, Poster)
[08] Y. Zheng, Q. Zhang*, and J. Zhu, “Spatial-spectral difference constraint under low-rank tensor factorization for HSI mixed noise removal,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Athens, Greece, 2024. (EI, Poster)
[07] Q. Zhang, and J. Zhu, “Early wildfire detection based on temporal, spatial and spectral information fusion,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Pasadena, USA, 2023. (EI, Poster)
[06] Q. Zhang, F. Sun, Q. Yuan, and L. Zhang, “Thick cloud removal for Sentinel-2 time-series images via combining deep prior and low-rank tensor completion,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Brussels, Belgium, pp. 2675-2678, 2021. (EI, Oral) [Slides]
[05] Q. Zhang, F. Sun, Q. Yuan, J. Li, H. Shen, and L. Zhang, “Combined the data-driven with model-driven stragegy: A novel framework for mixed noise removal in hyperspectral image,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Hawaii, USA, pp. 2667-2670, 2020. (EI, Oral) [Slides]
[04] Q. Zhang, Q. Yuan, J. Li, H. Shen, and L. Zhang, “Cloud and shadow removal for Sentinel-2 by progressively spatiotemporal patch group learning,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Yakohama, Japan, pp. 775-778, 2019. (EI, Oral) [Slides]
[03] Q. Zhang, Q. Yuan, H. Shen, and L. Zhang, “A unified spatial-temporal-spectral learning framework for reconstructing missing data in remote sensing images,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Valencia, Spain, pp. 4981-4984, 2018. (EI, Poster) [Slides]
[02] Y. Zhu, C. Yu, M. Song, Y. Wang, E. Zhao, H. Yu, and Q. Zhang, “Center category focusing transformer network for hyperspectral image classification,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Athens, Greece, 2024. (EI, Poster)
[01] Y. Yang, Y. Wang, E. Zhao, M. Song, and Q. Zhang, “A SWIN transformer-based fusion approach for hyperspectral image super-resolution,” Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), in Pasadena, USA, 2023. (EI, Poster)
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