梅晓光 讲师

梅晓光:博士,男,讲师。2007年本科毕业于华中科技大学获通信工程专业学士学位,2011年硕士毕业于华中师范大学通信与信息系统专业,随后于中船重工第七二二研究所从事舰船通信系统相关研制工作,2016年博士毕业于华中科技大学获电路与系统专业博士学位,期间荣获“博士研究生国家奖学金”。现为武汉大学电子信息学院讲师,主要从事红外高/超光谱应用中的计算机视觉、模式识别、机器学习等相关问题的研究工作。承担和参与军委科技委先进仿生技术主题、国家自然科学基金、国防探索、教育部支撑装备预研以及多项国家863计划项目的研究工作,在红外图像弱小目标探测识别以及红外超光谱图像分类的研究中取得了一定创新性成果,发表SCI论文20篇,获发明专利4项,获自动化学会奖励1项。担任IEEE Transactions on Geoscience and Remote Sensing、IEEE Geoscience and Remote Sensing Letters、Information Sciences、International Journal of Remote Sensing、Infrared Physics and Technology等期刊审稿人。(English Portal

教育背景

2003.9-2007.6 华中科技大学 通信工程  本科

2008.9-2011.6 华中师范大学 通信与信息系统  硕士

2012.9-2016.3 华中科技大学 电路与系统  博士

工作经历

2010.10-2012.8 中船重工第七二二研究所  嵌入式软件工程师

2016.6 –至今 武汉大学 电子信息学院  博士后

 

科研项目

主持项目

  1. 军委科技委先进仿生系统主题:17-163-12-ZT-004-076-01, 18-163-12-ZT-004-015-01, 仿生跳跃机器人三维视觉感知技术研究,2017-2020,150万

  2. 装备预研教育部联合基金青年人才项目:无人机红外-可见光融合图像超分辨率对地目标探测技术,2018-2020,80万

  3. 中国博士后科学基金:2017M612504,基于贝叶斯网络的高光谱盲解混方法研究,2017-2018,5万

  4. 中央高校自主科研项目,OP/FT-IR温室气体定量快速反演方法研究,2017/01-2018/12,15万

参与项目

  1. “十三五”装备预研领域基金:高性能红外-可见光融合探测技术,2017-2019

  2. 军委科技委前沿创新计划:****目标多波段实时融合探测技术,2017-2019

  3. 军委科技委前沿创新计划:****尾迹多阵列红外探测,2017-2018

  4. “十三五”海军装备预研创新:****目标探测技术,2017-2018

  5. 智能机器人与系统高精尖创新中心开放基金:可见光/红外图像融合的智能机器人感知系统,2017-2018

  6. “十三五”装备预研领域基金:一种基于红外超光谱谱指纹检测的新型光电探测感知技术,2016-2018

  7. 装备预研教育部联合基金:可见光/红外图像融合的****系统,2016.11-2018.10

  8. “十三五”装备预研领域基金:一种基于红外超光谱谱指纹检测的****技术,2016-2018

  9. “十三五”装备预研共用技术:红外****模块技术,2016-2020

  10. 国防探索研究重大项目:红外图谱成像****系统研究(项目编号:713112),2011-2015

  11. 863 计划项目:在轨操控过程中的****技术研究(项目编号:2011AA7044030),2011-2015

  12. 863 计划项目:**内波**探测技术(项目编号:2011AA7014047),2011-2015

  13. 863 计划项目:天基低轨远程空间****方法研究(项目编号:2015AA7046401),2015-2016

  14. 国家自然科学基金面上项目:红外超光谱谱指纹检测和识别海洋溢油污染方法研究,2013-2016

  15. 教育部支撑装备预先研究项目:基于超光谱目标探测识别信号处理机研究(项目编号:625010211)

发明专利

  1. 一种基于边界投影最优梯度的高光谱非线性解混方法,申请号:201510700049.2
  2. 基于局部线性迁移和刚性模型的图像特征匹配方法及系统,申请号:201510807463.3
  3. 用于目标识别的红外超光谱信号处理方法、处理机及系统,申请号:201410700672.3
  4. 一种基于鲁棒低秩张量的高光谱图像去噪方法,申请号:201510521057.0

学术论文

 
[1] Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., and Ma, J,”Spectral-Spatial Attention Networks for Hyperspectral Image Classification”. Remote Sensing 11. 2019 (SCI, IF=3.244, 二区)[code]

 [2] Ma, Y., Jin, Q., Mei, X*., Dai, X., Fan, F., Li, H., & Huang, J, “Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation”. Remote Sensing, 11(8), 911. 2019 (SCI, IF=3.244, 二区)[code]

 [3] X. Mei, Y. Ma, C. Li, F. Fan, J. Huang, and J. Ma, “Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation,” Neurocomputing, vol. 275, pp. 2783-2797, 2018.[code]

 [4] Y. Ma, C. Li, H. Li, X. Mei, and J. Ma, “Hyperspectral Image Classification with Discriminative Kernel Collaborative Representation and Tikhonov Regularization,” IEEE Geoscience and Remote Sensing Letters, 2018.

 [5] Q. Du, A. Fan, Y. Ma, F. Fan, J. Huang, and X. Mei*, “Infrared and Visible Image Registration Based on Scale-Invariant PIIFD Feature and Locality Preserving Matching,” IEEE Access, 2018.

 [6] J. Wang, J. Chen, H. Xu, S. Zhang, X. Mei*, J. Huang, and J. Ma, “Gaussian Field Estimator with Manifold Regularization for Retinal Image Registration,” Signal Processing, 2018-01-01 2018.

 [7] Y. Ma, C. Li, X. Mei*, C. Liu, and J. Ma, “Robust Sparse Hyperspectral Unmixing With l2,1 Norm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, pp. 1227-1239, 2017.[code]

 [8] H. Guo, Y. Ma, X. Mei*, and J. Ma, “Infrared and visible image fusion based on total variation and augmented Lagrangian,” Journal of the Optical Society of America A-Optics Image Science and Vision, vol. 34, pp. 1961-1968, 2017.

 [9] C. Li, Y. Ma, X. Mei*, F. Fan, J. Huang, and J. Ma, “Sparse Unmixing of Hyperspectral Data with Noise Level Estimation,” Remote Sensing, vol. 9, 2017.[code]

 [10] F. Fan, Y. Ma, C. Li, X. Mei, J. Huang, and J. Ma, “Hyperspectral image denoising with superpixel segmentation and low-rank representation,” Information Sciences, vol. 397, pp. 48-68, 2017.

 [11] Y. Ma, J. Wang, H. Xu, S. Zhang, X. Mei, and J. Ma, “Robust Image Feature Matching via Progressive Sparse Spatial Consensus,” IEEE Access, vol. 5, pp. 24568-24579, 2017.

[12] C. Li, Y. Ma, X. Mei, C. Liu, and J. Ma, “Hyperspectral Image Classification With Robust Sparse Representation,” IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 641-645, 2016.

[13] J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An Infrared Small Target Detecting Algorithm Based on Human Visual System,” IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 452-456, 2016.

[14] C. Li, Y. Ma, X. Mei, C. Liu, and J. Ma, “Hyperspectral Unmixing with Robust Collaborative Sparse Regression,” Remote Sensing, vol. 8, 2016.

[15] J. Huang, Y. Ma, X. Mei, and F. Fan, “A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA,” Infrared Physics & Technology, vol. 79, pp. 68-73, 2016.

[16] C. Li, Y. Ma, J. Huang, X. Mei, C. Liu, and J. Ma, “GBM-Based Unmixing of Hyperspectral Data Using Bound Projected Optimal Gradient Method,” IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 952-956, 2016.

[17] X. Mei, Y. Ma, F. Fan, C. Li, C. Liu, J. Huang, and J. Ma, “Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints,” International Journal of Remote Sensing, vol. 36, pp. 4724-4747, 2015.

[18] X. Mei, Y. Ma, C. Li, F. Fan, J. Huang, and J. Ma, “A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching,” Sensors, vol. 15, pp. 15868-15887, 2015.

[19] C. Li, Y. Ma, J. Huang, X. Mei, and J. Ma, “Hyperspectral image denoising using the robust low-rank tensor recovery,” Journal of the Optical Society of America A-Optics Image Science and Vision, vol. 32, pp. 1604-1612, 2015.

[20] T. Tian, X. Mei, Y. Yu, C. Zhang, and X. Zhang, “Automatic visible and infrared face registration based on silhouette matching and robust transformation estimation,” Infrared Physics & Technology, vol. 69, pp. 145-154, 2015.

[21] J. Huang, Y. Ma, F. Fan, X. Mei, and Z. Liu, “A scene-based nonuniformity correction algorithm based on fuzzy logic,” Optical Review, vol. 22, pp. 614-622, 2015.

[22] B. Zhou, S. Wang, Y. Ma, X. Mei, B. Li, H. Li, and F. Fan, “An infrared image impulse noise suppression algorithm based on fuzzy logic,” Infrared Physics & Technology, vol. 60, pp. 346-358, 2013.

联系方式

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