简历

袁强强,武汉大学测绘学院航空航天测绘研究所,教授, 博士生导师

研究方向:影像质量改善 ,多源数据融合,定量遥感,行星遥感,人工智能与机器学习

联系方式:qqyuan@sgg.whu.edu.cn, yqiang86@gmail.com;

办公地点:武汉大学信息学部教工39栋110办公室

个人简历:

    袁强强,1985年12月生,甘肃庆阳人,武汉大学测绘学院教授,博士生导师,测绘工程系副主任,国家优秀青年科学基金获得者,湖北省杰青。主要从事遥感影像质量改善、信息融合与定量遥感等方面的教学和研究工作。主持国家自然科学基金3项(优青、面上、青年),国家重点研发计划课题2项,湖北省杰出青年科学基金1项,及其他国家级和省部级项目10余项;在RSE、ESSD、ISPRS PE&RS、IEEE TGRS、GRL、JGR、IEEE TIP、JOH等国内外学术期刊上发表论文90余篇,论文总被引用4700余次(Google Scholar),其中单篇最高引用500余次, 3篇论文入选ESI热点论文,12篇论文入选ESI高被引论文。担任International Journal of Applied Earth Observation and Geoinformation(JAG)、European Journal of Remote Sensing、Remote Sesning、Computational Intelligence and Neuroscience 、IEEE Access、Sensors、《遥感学报》、《遥感技术与应用》等国内外期刊的副主编、编委或客座编辑,以及40余种国际期刊的审稿人。荣获测绘科技进步一等奖(2次)、湖北省自然科学二等奖、IEEE GRSL最佳审稿人、“湖北省优秀博士论文”等。

教学情况

  主讲《定量遥感基础》、《数字图像处理》、《 模式识别与机器学习》等本科专业核心课程

学术兼职

  1. International Journal of Applied Earth Observation and Geoinformation(JAG)期刊编委
  2. European Journal of Remote Sensing期刊副主编
  3. Remoe Sensing期刊编委
  4. Computational Intelligence and Neuroscience期刊学术编辑
  5. IEEE Access期刊副主编
  6. Sensors 期刊学术编辑
  7. Frontiers in Remote Sensing期刊副主编
  8. 《遥感学报》编委
  9. 《遥感技术与应用》编委
  10. 中国图形图像学会遥感专委会委员

奖励荣誉

  1. IEEE GRSL期刊最佳审稿人,2019
  2. 测绘科技进步一等奖,多成因辐射退化遥感数据的质量改善理论、方法与应用,2017,排名3
  3. 测绘科技进步一等奖,高光谱遥感数据分类关键技术及其典型应用,2018,排名10
  4. 湖北省自然科学二等奖,遥感图像结构化表征与信息挖掘 ,2018,排名4
  5. 湖北省优秀博士论文,2013
  6. ISPRS 2016青年论坛最佳论文奖,2016
  7. 南方测绘杯全国测绘学科青年教师教学竞赛二等奖,2019
  8. 武汉大学青年教师教学竞赛三等奖,2018
  9. 武汉大学测绘学院青年教师教学竞赛一等奖,2018

主要学术论著

        https://www.researchgate.net/profile/Qiangqiang_Yuan 

2021

  1. 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), 2021
  2. Y.Wang, Q.Yuan*, L.Zhu, and  L. Zhang,  Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model. Geoscience Frontiers.2021
  3. Q. Yang, B. Wang, Y. Wang, Q. Yuan*, C. Jin, J. Wang, S. Li, M. Li, T. Li, S. Liu, H. Shen, and L. Zhang, Global air quality change during COVID-19: a synthetic analysis of satellite, reanalysis and ground station data, Environmental Research Letters,2021
  4. 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, in press, 2021.
  5. Y. Wang, Q. Yuan*, T.Li, L. Zhu, L. Zhang, Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP, ISPRS Journal of Photogrammetry and Remote Sensing,  2021
  6. 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,2021
  7. B. Wang, Q. Yuan*, Q. Yang, L. Zhu, T. Li, and L. Zhang, Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long   short-term memory network, Environmental Pollution, Volume 271,2021
  8. Y. Wang, Q. Yuan*, T. Li, S. Tan, L. Zhang, Full-coverage spatiotemporal mapping of ambient PM2.5 and PM10 over China from Sentinel-5P and assimilated datasets: Considering the precursors and chemical compositions,Science of The Total Environment, 2021,148535
  9. D. Liu, J. Li and Q. Yuan*, A Spectral Grouping and Attention-Driven Residual Dense  Network for Hyperspectral Image Super-Resolution, IEEE Transactions Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3049875,2021
  10. S.Zhao,Q.Yuan*,J.Li,Y.Hu,X.Liu,and L.Zhang, A Fast and Effective Irregular Stripe Removal Method for Moon Mineralogy Mapper (M3), IEEE Transactions on Geoscience and Remote Sensingdoi:  10.1109/TGRS.2021.3054661,2021
  11. Y. Xiao, X. Su, Q. Yuan, D. Liu, H. Shen and L. Zhang, “Satellite Video Super-Resolution via Multiscale Deformable Convolution Alignment and Temporal Grouping Projection,”  IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3107352,2021
  12. T. Li, H. Shen, Q. Yuan and L. Zhang, “A Locally Weighted Neural Network Constrained by Global Training for Remote Sensing Estimation of PM2.5,”  IEEE Transactions on Geoscience and Remote Sensing, 2021
  13. J. Xiao, J. Li, Q. Yuan and L. Zhang, “A Dual-UNet With Multistage Details Injection for Hyperspectral Image Fusion,”  IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3101848, 2021
  14. Y.Chen, S. Cui, P. Chen, Q. Yuan, P. Kang and L. Zhu, An LSTM-based neural network method of particulate pollution forecast in China, Environmental Research Letters, 2021
  15. J. Xiao, J. Li, Q. Yuan, M. Jiang and L. Zhang, “Physics-Based GAN With Iterative Refinement Unit for Hyperspectral and Multispectral Image Fusion,”  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 6827-6841, 2021, doi: 10.1109/JSTARS.2021.3075727.
  16. Z. Nong, X. Su, Y. Liu, Z. Zhan and Q. Yuan, “Boundary-Aware Dual-Stream Network for VHR Remote Sensing Images Semantic Segmentation,”  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 5260-5268, 2021, doi: 10.1109/JSTARS.2021.3076035.

2020

  1. Q. Yuan, H. Shen, T. Li, Z. Li, S. Li, Y. Jiang, H. Xu, W. Tan, Q. Yang, J. Wang, J. Gao, L. L. Zhang, Deep learning in environmental remote sensing: Achievements and challenges, Remote Sensing of Environment, Volume 241,2020, 111716(ESI 热点、高被引论文)
  2. Y.Wang, Q.Yuan*, T.Li, , S.Tan, L. Zhang, Estimating daily full-coverage and high-accuracy 5-km ambient particulate matters across China: considering their precursors and chemical compositions, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1004, 2020.
  3. Q. Yang, Q. Yuan*, T. Li, L.Yue, Mapping PM2.5 concentration at high resolution using a cascade random forest based downscaling model: Evaluation and application, Journal of Cleaner Production,Volume 277,2020,123887
  4. Q. Yang, Q.Yuan*, L. Yue, T.Li, H. Shen, L. Zhang, Mapping PM2.5 concentration at a sub-km level resolution: A dual-scale retrieval approach,ISPRS Journal of Photogrammetry and Remote Sensing, Volume 165,2020, Pages 140-151
  5. R. Luo, Q. Yuan, L. Yue and J. X. Shi, “Monitoring recent lake variations under climate change around the Altai Mountains using multi-mission satellite data,”  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2020.3035872. 2020
  6. S. Zhao, J. Li, Q. Yuan, H. Shen and L. Zhang, Can Terrestrial Restoration Methodologies be Transferred to Planetary Hyperspectral Imagery? A Quantitative Intercomparison and Discussion,”  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5759-5775, 2020
  7. J. Wei, W. Huang, Z. Li, L. Sun, X. Zhu, Q. Yuan, L. Liu, M. Cribb,Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches,Remote Sensing of Environment,
    Volume 248,2020,112005
  8. S. Zhao, D. Liu,  Q.Yuan*, J. Li,  A Global Gravity Reconstruction Method for Mercury Employing Deep Convolutional Neural Network. Remote Sens. 2020, 12, 2293.
  9. T.Li, Y. Wang,  Q. Yuan*, Remote Sensing Estimation of Regional NO2 via Space-Time Neural Networks. Remote Sens. 2020, 12, 2514.
  10. T. Li, H. Shen, Q. Yuan, L. Zhang, Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 167,2020,Pages 178-188,
  11. Y. Wang, Q. Yuan*,  H. Shen, L. Zheng, and L. Zhang, “ Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: evaluation, comparison, and merging,” Atmospheric Environment, in press, 2020.
  12. 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, in press, 2020.
  13. J. Wang, Q. Yuan*, H. Shen, T. Liu, T. Li, L. Yue, X. Shi, L. Zhang, Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach, Journal of Hydrology, Volume 585,2020,124828
  14. Q. Zhang, Q. Yuan*, J. Li, Z. Li, H. Shen, L. Zhang, Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning,ISPRS Journal of Photogrammetry and Remote Sensing,Volume 162, 2020, Pages 148-160
  15. H. Shen, L. Lin, J. Li, Q. Yuan, L. Zhao, A residual convolutional neural network for polarimetric SAR image super-resolution, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 161,2020, Pages 90-108.
  16. Q. Yang, Q. Yuan*, L. Yue, T. Li, Investigation of the spatially varying relationships of PM2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression, Environmental Pollution, Volume 262,2020,114257
  17. S. Zhang, Q. Yuan*, J. Li, J. Sun and X. Zhang, “Scene-Adaptive Remote Sensing Image Super-Resolution Using a Multiscale Attention Network,” IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2020.2966805, 2020. In press.
  18. X. Meng, Y. Xiong, F.Shao, H. Shen, W. Sun, G.Yang, Q.Yuan, R.Fu, H.Zhang, A Large-Scale Benchmark Data Set for Performance Evaluation of Pansharpening, IEEE Geoscience and Remote Sensing Magazine. 2020.(封面文章,ESI 高被引论文)
  19. J. Gao, Q.Yuan, J. Li, H. Zhang, X. Su, Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks. Remote Sens.2020, 12, 191.
  20. Q. Yuan, H. Xu, H. Shen, L. Zhang, Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements,Journal of Hydrology, Vol. 580, 124351. 2020.
  21. T. Li, H. Shen, C. Zeng and Q. Yuan, A validation approach considering the uneven distribution of ground stations for satellite-based PM2.5 estimation,  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020.
  22. M.Jiang, H.Shen,J.Li, Q.Yuan,L.Zhang, A Differential Information Residual Convolutional Neural Network for Pansharpening, ISPRS Journal of Photogrammetry and Remote Sensing, 2020

2019 

  1. 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, vol. 57, no. 2, pp. 1205-1218, 2019. (ESI 高被引论文)
  2. Q.. Yuan, S. Li, L. Yue, T. Li, H. Shen, and L. Zhang, “Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point-Surface Fusion of MODIS Products and GNSS-IR Observations,” Remote Sensing, 2019.
  3. Y. Wang, Q.Yuan, T. Li, H. Shen, L.Zheng, L. Zhang,Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation,ISPRS Journal of Photogrammetry and Remote Sensing,Volume 157,2019,Pages 1-12.
  4. Q. Cheng, Q. Yuan, M. K. Ng, H. Shen and L. Zhang, “Missing Data Reconstruction for Remote Sensing Images With Weighted Low-Rank Tensor Model,” IEEE Access, vol. 7, pp. 142339-142352, 2019.
  5. Z. Gu, Z.Zhan, Q.Yuan, L.Yan, Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network. Remote Sens.2019, 11, 3008.
  6. J. Li, X. Liu, Q. Yuan*, H. Shen, and L. Zhang, “Antinoise Hyperspectral Image Fusion by Mining Tensor Low-Multilinear-Rank and Variational Properties,”IEEE Transactions on Geoscience and Remote Sensing, in press, 2019.
  7. Y. Wang, Q. Yuan*, T. Li, H. Shen, L. Zheng, and L. Zhang, “Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces,” Atmospheric Environment, vol. 200, pp. 280-301, 2019. (ESI 高被引论文)
  8. Q. Yang, Q. Yuan*, L. Yue, T. Li, H. Shen, and L. Zhang, “The Relationships Between PM2.5 and Aerosol Optical Depth (AOD) in Mainland China: About and Behind the Spatio-Temporal Variations,” Environmental Pollution, vol. 248, pp. 526-535, 2019.
  9. H. Fan, J. Li, Q. Yuan*, X. Liu, and M. Ng, “Hyperspectral Image Denoising with Bilinear Low Rank Matrix Factorization,” Signal Processing, vol. 163, pp. 132-152, 2019.
  10. Q. Zhang, Q. Yuan, J. Li, X. Liu, H. Shen, and L. Zhang, “Hybrid Noise Removal in Hyperspectral Imagery with a Spatial-Spectral Gradient Network,” IEEE Transactions on Geoscience and Remote Sensing, Vol.57, No.10, PP. 7317-7329.2019
  11. X. Meng, H. Shen, Q. Yuan, H. Li, L. Zhang, and W. Sun, “Pansharpening for Cloud-Contaminated Very High-Resolution Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 5, pp. 2840-2854, 2019.
  12. H. Shen, M. Jiang, J. Li, Q. Yuan, Y. Wei, and L. Zhang, Spatial-Spectral Fusion by Combining Deep Learning and Variational Model, IEEE Transactions on Geoscience and Remote Sensing, in press, 2019.

2018

  1. Q. Yuan, Y. Wei, X. Meng, H. Shen, and L. Zhang, “A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening,”IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 3, pp. 978-989, 2018. (ESI 高被引论文)
  2. 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, vol. 56, no. 8, pp. 4274-4288, 2018. (ESI 高被引论文)
  3. H. Shen, T. Li, Q. Yuan*, and L. Zhang, “Estimating Regional Ground‐Level PM2. 5 Directly from Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks,” Journal of Geophysical Research: Atmospheres, vol. 123, no. 24, pp. 13875-13886, 2018.
  4. Q. Zhang, Q. Yuan*, J. Li, Z. Yang, and X. Ma, “Learning a Dilated Residual Network for SAR Image Despeckling”, Remote Sensing, vol. 10, no. 2, pp. 196, 2018
  5. H. Xu, Q. Yuan*, T. Li, H. Shen, L. Zhang, and H. Jiang, “Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.,” Remote Sensing, vol. 10, no. 9, pp. 1351, 2018.
  6. X. Liu, H. Shen, Q. Yuan, X. Lu, and C. Zhou, “A Universal Destriping Framework Combining 1-D and 2-D Variational Optimization Methods,”IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 2, pp. 808-822, 2018.
  7. 沈焕锋,袁强强,李杰,岳林蔚,张良培, 遥感数据质量改善之信息复原. 科学出版社, 2018.

2017

  1. M.Ng, Q. Yuan*, L. Yan and J. Sun, “An Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images with Missing Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 6, pp. 3367-3381, 2017.
  2. Q. Yang, Q. Yuan*, T. Li, H. Shen, and L. Zhang, “The Relationships Between PM2. 5 and Meteorological Factors in China: Seasonal and Regional Variations,” International Journal of Environmental Research and Public Health, vol. 14, no. 12, pp. 1510-1528, 2017
  3. Y. Wei, Q. Yuan*, H. Shen, and L. Zhang, “Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network,” IEEE Geoscience and Remote Sensing Letters, vol.14, no.10, pp. 1795-1799, 2017. (ESI 高被引论文)
  4. T. Li, H. Shen, Q. Yuan, X. Zhang, and L. Zhang, “Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach,” Geophysical Research Letters, vol. 44, pp. 1-9, 2017.(ESI 高被引论文)
  5. T. Li, H. Shen, C. Zeng, Q. Yuan, and L. Zhang, “Point-Surface Fusion of Station Measurements and Satellite Observations for Mapping PM2.5 Distribution in China: Methods and Assessment,” Atmospheric Environment, vol. 152, pp. 477-489, 2017.(ESI 高被引论文)
  6. L.Yue, H. Shen, L. Zhang, X. Zheng, F. Zhang, and Q. Yuan, “High-Quality Seamless DEM Generation Blending SRTM-1, ASTER GDEM V2 and Icesat/GLAS Observations,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 123, pp. 20-34, 2017.
  7. Q. Wang, L. Yan, Q. Yuan, and Z. Ma, “An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery,” Remote Sensing, vol. 9, no. 5, 2017.

2016

  1. J. Li, Q. Yuan*, H. Shen, and L. Zhang, “Noise Removal from Hyperspectral Image with Joint Spectral-Spatial Distributed Sparse Representation,”IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 9, pp. 5425-5439, 2016.
  2. J. Li, Q. Yuan*, H. Shen, X. Meng, and L. Zhang, “Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial-Spectral Group Sparsity,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 9, pp. 1250-1254, 2016.
  3. X. Liu, X. Lu, H. Shen, Q. Yuan, Y. Jiao, L. Zhang, “Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties,”IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 3049-3060, 2016.
  4. Y. Xie, Y. Qu, D. Tao, W. Wu, Q. Yuan and W. Zhang, “Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4642-4659, 2016.
  5. C. Jiang, H. Zhang, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral Image Denoising with a Combined Spatial and Spectral Weighted Hyperspectral Total Variation Model,” Canadian Journal of Remote Sensing, vol. 42, no. 1, pp. 53-72, 2016
  6. H. Shen, L. Peng, L. Yue, Q. Yuan, and L. Zhang, “Adaptive Norm Selection for Regularized Image Restoration and Super-Resolution,” IEEE Transactions on Cybernetics, vol. 46, no. 6, pp. 1388-1399, 2016
  7. H. Li, X. Wang, H. Shen, Q. Yuan, and L. Zhang, “An Efficient Multi-Resolution Variational Retinex Scheme for the Radiometric Correction of Airborne Remote Sensing Images,” International Journal of Remote Sensing, vol. 37, no. 5, pp. 1154-1172, 2016.
  8. L. Yue, H. Shen, J. Li, Q. Yuan, H. Zhang, and L. Zhang, “Image Super-Resolution: The Techniques, Applications, and Future,” Signal Processing, vol. 128, pp. 389-408, 2016. (ESI热点、高被引论文)

2015

  1. L. Yue, H. Shen, Q. Yuan, and L. Zhang, “Fusion of Multi-scale DEMs using Regularized Super-resolution Methods,” International Journal of Geographical Information Science, vol. 29, no. 12, pp. 2095-2120, 2015.
  2. J. Li, Q. Yuan*, H. Shen, and L. Zhang, “Hyperspectral Image Recovery Employing a Multidimensional Nonlocal Total Variation Model,” Signal Processing, vol. 111, pp. 230-248, 2015.

2014

  1. Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral Image Denoising With a Spatial-Spectral View Fusion Strategy,”IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2314-2325, 2014.
  2. H. Shen, W. Zhao, Q. Yuan*, and L. Zhang, “Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization,”Remote Sensing, vol. 6, no. 8, pp. 7491-7521, 2014.
  3. H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral Image Restoration Using Low-Rank Matrix Recovery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 8, pp. 4729-4743, 2014. (ESI热点、高被引论文)
  4. Q. Cheng, H. Shen, L. Zhang, Q. Yuan, and C. Zeng, “Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 92, pp. 54-68, 2014.
  5. X. Li, H. Shen, L. Zhang, H. Zhang, Q. Yuan, and G. Yang, “Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multitemporal Dictionary Learning,”IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 7086-7098, 2014
  6. X. Li, H. Shen, L. Zhang, H. Zhang, and Q. Yuan, “Dead Pixel Completion of Aqua MODIS Band 6 using a Robust M-Estimator Multi-Regression,”IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 4, pp. 768-772, 2014.
  7. H. Shen, H. Li, Y. Qian, L. Zhang, and Q. Yuan, “An Effective Thin Cloud Removal Procedure for Visible Remote Sensing Images,”ISPRS Journal of Photogrammetry and Remote Sensing, vol. 96, pp. 224-235, 2014
  8. L. Yue, H. Shen, Q. Yuan, and L. Zhang, “A Locally Adaptive L1-L2 Norm for Multi-Frame Super-Resolution of Images with Mixed Noise and Outliers,” Signal Processing, vol. 105, pp. 156-174, 2014.
  9. X. Lan, H. Shen, L. Zhang, and Q. Yuan, “A Spatially Adaptive Retinex Variational Model for the Uneven Intensity Correction of Remote Sensing Images,” Signal Processing, vol. 101, pp. 19-34, 2014.

2013

  1. Q. Yuan, L. Zhang, and H. Shen, “Regional Spatially Adaptive Total Variation Super-resolution with Spatial Information Filtering and Clustering,” IEEE Transactions on Image Processing, vol. 22, no. 6, pp. 2327-2342, 2013.
  2. X. Lan, L. Zhang, H. Shen, Q. Yuan, and H. Li, “Single Image Haze Removal Considering Sensor Blur and Noise,” EURASIP Journal on Advances in Signal Processing, 2013.

2012

  1. Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral Image Denoising Employing a Spectral-spatial Adaptive Total Variation Model,”IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3660-3677, 2012. (ESI 高被引论文)
  2. Q. Yuan, L. Zhang, and H. Shen, “Multi-frame Super-Resolution Employing a Spatially Weighted Total Variation Model,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 3, pp. 379-392, 2012.
  3. 张良培, 沈焕锋, 张洪艳, 袁强强. 图像超分辨率重建. 北京: 科学出版社, 2012.

2011

  1. L. Zhang, Q. Yuan, H. Shen, and P. Li, “Multi-Frame Image Super-Resolution Adapted with Local Spatial Information,” Journal of the Optical Society of America A, vol. 28, no. 3, pp. 381-390, 2011.

2010

  1. Q. Yuan, L. Zhang, H. Shen, and P. Li, “Adaptive Multiple-Frame Image Super-Resolution Based on U-curve,”IEEE Transactions on Image Processing, vol. 19, no. 12, pp. 3157-3170, 2010.