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杨文,男,1976年生,武汉大学电子信息学院教授,博士生导师。1998年,2001年和2004年先后在武汉大学获得工学学士,工学硕士和工学博士学位。法国应用数学与计算机科学实验室(Laboratoire Jean Kuntzmann)访问学者。 长期从事遥感图像处理与分析以及机器视觉等领域的研究工作,在包括 IEEE Trans. on Image Processing (TIP) , IEEE Trans. on Geoscience and Remote Sensing (TGRS) 等国际权威期刊和IGARSS、EUSAR、ICIP、ICPR等相关国际会议上发表学术论文100余篇。 获得湖北省科学技术奖励自然科学奖三等奖、湖北省优秀学士/硕士学位论文指导教师等荣誉。 详细个人简历


  • 2001.09-2004.12,武汉大学 电子信息学院,通信与信息系统,工学博士.
  • 1998.09-2001.06,武汉大学 测绘遥感信息工程国家重点实验室 计算机应用专业,工学硕士.
  • 1994.09-1998.06,武汉大学 电子信息学院,电子仪器及测量技术,工学学士.


  • 2013.11 至今,武汉大学电子信息学院,教授/博士生导师.
  • 2010.11-2013.11,武汉大学测绘遥感信息工程国家重点实验室,博士后研究员.
  • 2008.09-2009.09,法国应用数学与计算机科学实验室(Laboratoire Jean Kuntzmann), 访问学者/博士后.
  • 2006.11-2013.10,武汉大学电子信息学院,副教授/硕士生导师.


  • IEEE资深会员 (Senior Member)
  • 中国自动化学会模式识别和机器智能专业委员会委员
  • 中国电子学会高级会员


  • 湖北省科学技术奖励自然科学奖三等奖,2012
  • 湖北省优秀硕士学位论文指导教师,2017
  • 湖北省优秀学士学位论文指导教师,2006,2007,2010,2013,2014,2016


  • 图像处理与机器视觉.
  • 机器学习及其在图像大数据中的应用.
  • 无人机系统智能信息处理及应用.


  • XiangLi Yang, Wen Yang*, Hui Song, et al. Polarimetric SAR Image Classification Using Geodesic Distances and Composite Kernels,
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.11, No.5, pp. 1606–1614, 2018.
  • Neng Zhong, Wen Yang*, Anoop Cherian, et al. Unsupervised Classification of Polarimetric SAR Images via Riemannian Sparse Coding.
    IEEE Trans. on Geoscience and Remote Sensing,Vol.55, No.9, pp. 5381–5390, 2017.
  • Tianheng Yan, Wen Yang*, Xiangli Yang, et al. Polarimetric SAR Despeckling by Integrating Stochastic Sampling and Contextual Patch Dissimilarity Exploration.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.10, No.6, pp. 2738–2753, 2017.
  • Wen Yang*, Xiangli Yang, Tianheng Yan, et al. Region-based Change Detection for Polarimetric SAR Images Using Wishart Mixture Models.
    IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.11, pp.6746–6756, 2016
  • Huai Yu, Wen Yang*, Gui-Song Xia, and Gang Liu. A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification.
    Remote Sensing, Vol.8, No.3, pp.259, 2016
  • Gui-Song Xia, Gang Liu, Wen Yang, et al. Meaningful object segmentation from SAR images via a multi-scale NLAC model.
    IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.4, pp.2108-2123, 2016.
  • Wen Yang*, Xiaoshuang Yin, Guisong Xia. Learning High-level Features for Satellite Image Classification With Limited Labeled Samples.
    IEEE Trans. on Geoscience and Remote Sensing, Vol. 53, No. 8, pp.4472–4482, 2015.
  • Wen Yang*, Hui Song, Xiaojing Huang, et al. Change Detection in High Resolution SAR Images Based on Jensen-Shannon Divergence and Hierarchical Markov Model.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.7, No.8, pp. 3318–3327,2014.
  • Wen Yang*, Xiaoshuang Yin, Hui Song, et al. Extraction of Built-up Areas from Fully Polarimetric SAR Imagery via PU Learning.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 4, pp.1207–1216, 2014.
  • Wen Shao, Wen Yang*, Gui-Song Xia. Extreme value theory-based calibration for multiple feature fusion in high-resolution satellite scene classification.
    International Journal of Remote Sensing, Vol. 34, No. 23, pp.8588–8602, 2013.
  • Kan Xu, Wen Yang*, Gang Liu, et al. Unsupervised Satellite Image Classification Using Markov Field Topic Model.
    IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 1, pp. 130–134, 2013.
  • Guofeng Sheng, Wen Yang*, Xinping Deng, et al. Coastline Detection in Synthetic Aperture Radar(SAR) Images by Integrating Watershed Transformation and Controllable Gradient Vector Flow (GVF) Snake Model.
    IEEE Journal of Oceanic Engineering, Vol.37, No.3, pp.375–383, Jul. 2012.
  • Wen Yang*, Ying Liu, Guisong Xia, et al. Statistical mid-level features for building-up area extraction from full polarimetric SAR imagery.
    Progress In Electromagnetics Research, Vol. 132, pp. 233–254, 2012.
  • Wen Yang*, Dengxin Dai, Bill Triggs, Gui-Song Xia. SAR-based terrain classification using weakly supervised hierarchical Markov aspect models.
    IEEE Trans. on Image Processing, Vol.21, No.9, pp.4232-4243, 2012.