自然与人类活动模拟

郑晨

  

郑晨,男,1985119日出生于河南开封(更新于2021-06-04

手机:15039017689 电子邮件:10100075@vip.henu.edu.cn

 

l  教育经历:

2009/92012/6,武汉大学,概率论与数理统计/测绘遥感国家重点实验室,博士,导师:胡亦钧/秦前清

2007/92009/6,武汉大学,概率论与数理统计/测绘遥感国家重点实验室,硕士,导师:胡亦钧/秦前清

2003/92007/7,河南大学,信息与计算科学,学士

 

  任职经历:

2018/5至现在,河南大学,数学与统计学院统计系,副教授

2019/92020/2,加拿大新布伦瑞克大学,测绘学院,访问学者

2015/102016/10,加拿大新布伦瑞克大学,测绘学院,博士后

2014/62018/11,中国科学院,国家天文台,博士后

2012/72018/4 河南大学,数学与统计学院统计系,讲师

 

  最近五(5)年从事过的科研项目:

1.        主持人:基于深度随机场的高空间分辨率遥感影像多语义分割(国家自然基金面上项目,批准号:4177137563万元,2018/01 - 2021/12.

2.        主持人:像元与对象协同的遥感影像多语义尺度统计分割研究(国家自然基金青年项目,批准号:4130147025万元,2014/01 - 2016/12.

 

  最近五(5)年发表的论文:

1.     Zheng, C(郑晨)., Chen, Y. C., Shao, J., Wang, L. G *., 2021. An MRF-based multigranularity edge-preservation optimization for semantic segmentation of remote sensing images. IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.3058939.

2.        Zheng, C(郑晨)., Zhang, Y., Wang, L. G*., 2020. Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, DOI:10.1109/TGRS.2020.3033293.

3.        Zheng, C(郑晨)., Pan, X.X., Chen, X.H., Yang, X.H., Xin, X., Su, L.M., 2019, An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery. Remote Sensing, 11(23), 2878.

4.        Zheng, C(郑晨)., Wang, L. G*., Chen, X.H., 2019. A Hybrid Markov Random Field Model With Multi-Granularity Information for Semantic Segmentation of Remote Sensing Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(8): 2728 - 2740.

5.        Zheng, C(郑晨)., Yao, H.T.,2019. Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients. International Journal of Remote Sensing, 40(11): 4441-4472.

6.        Zheng, C(郑晨)*., Zhang, Y., Wang, L. G., 2017. Semantic Segmentation of Remote Sensing Imagery Using Object-based Markov Random Field Model with Auxiliary Label Fields. IEEE Transactions on Geoscience and Remote Sensing, 55(5):3015-3028.

7.        Wang, L. G., Xin Huang, X., Zheng, C(郑晨)., Zhang, Y., 2017. A Markov random field integrating spectral dissimilarity and class co-occurrence dependency for remote sensing image classification optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 128:223-239.

8.        Chen, X.H., Zheng, C(郑晨)*., Yao, H.T., Wang, B.X., 2017, Image segmentation using a unified Markov random field model. IET Image Processing, 11(10): 860-869.

9.        Zheng, C(郑晨)*., Zhang, Y., Wang, L.G.,2016. Multi-layer Semantic Segmentation of Remote Sensing Imagery Using Hybrid Object-based Markov Random Field Model. International Journal of Remote Sensing, 37(23): 5505-5532.

10.     Zheng, C(郑晨)*., Ping, J.S., Wang, M.Y., 2016, Hierarchical Classification for the Topography Analysis of Asteroid (4179) Toutatis from the Chang'E-2 Images. ICARUS, 278:119-127.