马丽

发布人:王懿鸿发表时间:2021-03-24点击:


马丽(1982.3-),博士,副教授, 硕士生导师。2008-2010年美国普度大学访问学者,2010华中科技大学获博士学位2018美国密西西比州立大学访问学者。主持国家自然科学基金面上项目1项,青年基金项目1项,中国地质大学摇篮计划1项,参与国家自然科学基金重大研究计划2项,主要进行遥感图像分析,机器学习,深度学习等方面的研究,在国内外重要学术刊物及学术会议上发表多篇学术论文,论文总被引次数557次(Web of Science统计),受邀撰写外文书籍1章节。

联系方式:

电子信箱: maryparisster@gmail.com; 123378879@qq.com

办公室:教二楼523


主要经历:

2011.3-至今, 中国地质大学,威尼斯欢乐娱人城3328,通信工程系

2018.3-2018.8美国密西西比州立大学,访问学者

2008.9-2010.9美国普度大学,遥感应用实验室,访问学者

2006.9-2011.3,华中科技大学,图像所,模式识别与智能系统专业,工学博士学位。

2004.9-2006.6山东大学,控制科学与工程学院,模式识别与智能系统专业,工学硕士学位。

2000.9-2004.6山东大学,控制科学与工程学院,生物医学工程专业,工学学士学位。


主要研究方向:

1、 遥感图像分析

高光谱遥感图像分类;高光谱遥感图像目标检测;高分辨率遥感影像处理

2、 机器学习算法

流形学习;迁移学习;稀疏表达;深度学习


发表论文:

期刊论文(第一作者、通讯作者(*)

[1] Z. Liu, L. Ma, and Q. Du, “Class-wise distribution adaptation for unsupervised classification of hyperspectral remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 508521, January 2021. (SCI, T2) IF=5.855

[2] W. Wang, L. Ma, M. Chen, and Q. Du, “Joint correlation alignment based graph neural network for domain adaptation of multitemporal hyperspectral remote sensing images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, DOI: 10.1109/JSTARS. 2021.3063460. (SCI, T2) IF=3.827

[3] H. Wei, L. Ma, Y. Liu, and Q. Du, “Combining multiple classifiers for domain adaptation of remote sensing image classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 1832–1847, January 2021. (SCI, T2) IF=3.827

[4] M. Chen, L. Ma, W. Wang, and Q. Du, “Augmented associative learning-based domain adaptation for classification of hyperspectral remote sensing images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 62366248, October 2020. (SCI, T2) IF=3.827

[5] L. Ma, M. M. Crawford, L. Zhu and Y. Liu, “Centroid and covariance alignment-based domain adaptation for unsupervised classification of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 4, pp. 23052323, April 2019. (SCI, T2) IF=5.855Web of Science引用

[6] L. Ma, C. Luo, J. Peng and Q. Du, “Unsupervised manifold alignment for cross-domain classification of remote sensing images,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 10, pp. 16501654, October 2019. (SCI, T3) IF=3.833

[7] L. Zhou and L. Ma, “Extreme learning machine-based heterogeneous domain adaptation for classification of hyperspectral images, IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 11, pp. 17811785, November 2019. (SCI, T3) IF=3.833

[8] C. Luo and L. Ma, Manifold regularized distribution adaptation for classification of remote sensing images,” IEEE Access, vol. 6, no. 1, pp. 4697-4708, 2018. (SCI, T3) IF=3.745

[9] Li Ma, Jiazhen Song, Deep neural network-based domain adaptation for classification of remote sensing images, Journal of Applied Remote Sensing, 2017, 11(4), 042612. (SCI, T4)

[10] Li Ma, Xiaofeng Zhang, Xin Yu, Dapeng Luo, Spatial Regularized Local Manifold Learning for Classification of Hyperspectral Images, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2016, 9(2): 609- 624. (SCI, T2) IF=3.827Web of Science引用

[11] L. Ma, A. Ma, C. Ju, and X. Li, "Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification," Pattern Recognition Letters, vol. 83, pp. 133-142, 2016. (SCI, T3) IF=3.255Web of Science引用

[12] L. Zhu, and L. Ma, "Class centroid alignment based domain adaptation for classification of remote sensing images," Pattern Recognition Letters, vol. 83, pp. 124-132, 2016. (SCI, T3) IF=3.255Web of Science引用

[13] C. Xing, L. Ma, and X. Yang, "Stacked denoise autoencoder based feature extraction and classification for hyperspectral images," Journal of Sensors, Article ID 3632943, 1:10, 2016. Web of Science引用

[14] L. Ma, M. M. Crawford, X. Yang, and Y. Guo, “Local manifold learning based graph construction for semisupervised hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 28322844, May 2015. (SCI, T2) IF=5.855

[15] Li Ma, Melba. M. Crawford, and Jinwen Tian, “Local manifold learning-based k-nearest-neighbor for hyperspectral image classification”. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(11): 4099-4199. (SCI, T2) IF=5.855

[16] Li Ma, Melba. M. Crawford, and Jinwen Tian, “Generalised supervised local tangent space alignment for hyperspectral image classification”. Electronics Letters, 2010, 46(7): 497-498. (SCI, T3) 引用

[17] L. Ma, Melba M. Crawford, and Jinwen Tian, “Anomaly detection for hyperspectral images based on robust locally linear embedding”. Journal of Infrared Millimeter and Terahertz Waves, 2010, 31(6): 753-762. (SCI, T4) 引用

[18] 邵远杰,吴国平,马丽*. 基于属类概率距离构图的半监督学习在高光谱遥感图像分类中的应用,测绘学报,2014, 43(11): 82-89.

[19] 马丽*,鞠才,朱菲. 一种面向异常检测的高光谱图像降维算法,测绘科学,2015, 39(7).

[20] 王小攀,马丽*, 刘福江. 一种基于线性邻域传播的加权k近邻算法,计算机工程,2013, 39(7): 288-292.

[21] 马丽*,田金文. 基于局部能量最大可分的高光谱图像异常检测算法,遥感学报,2008,12(3): 420-427.

[22] 马丽*,常发亮,乔谊正,基于改进的均值漂移算法和粒子滤波算法的目标跟踪,模式识别与人工智能,2006,19,(6): 787-793. (EI)


期刊论文(合作作者)

[1] Jun Chen, Jiayi Ma, Changcai Yang, Li Ma, and Sheng Zheng. Non-rigid point set registration via coherent spatial mapping, Signal Processing, 2015,106: 62-72.T2

[2] Jiayi Li, Hongyan Zhang, Liangpei Zhang, and Li Ma, “Hyperspectral anomaly detection by the use of background joint sparse representation,” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2015,8(6): 2523-2533. SCIT2

[3] Xiaoyong Bian, Xiaolong Zhang, Renfeng Liu, Li Ma, Xiaowei Fu. Adaptive classification of hyperspectral images using local consistency. Journal of Electronic Imaging, 2014, 23(6): 063014-1-17.

[4] Faliang Chang, Li Ma, Yizheng Qiao, “Target tracking under occlusion by combining integral-intensity-matching with multi-block-voting,” Lecture Notes in Computer Science, ICIC 2005, 3644(1): 77-86.

[5] 常发亮,马丽,乔谊正,复杂环境下基于自适应粒子滤波器的目标跟踪,电子学报,2006, 34(12):2150-2153. (EI)

[6] 常发亮,马丽,乔谊正,遮挡情况下基于特征相关匹配的目标跟踪算法,中国图象图形学报2006,11(6):817-822.

[7] 常发亮,马丽,乔谊正,遮挡情况下的视觉目标跟踪方法研究,控制与决策,2006,21(5): 503-507. (EI)

[8] 常发亮,马丽,乔谊正,视频序列中面向人的多目标跟踪算法,控制与决策, 2007,22(4):418-422. (EI)


会议论文(第一作者、通讯作者):

[1] H. Wei, L. Ma, and X. Liu, “Multi-classifiers consistency based unsupervised manifold alignment for classification of remote sensing images,” IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, September 26 - October 2, 2020, DOI: 10.1109/IGARSS39084. 2020.9323841. (研究生参加IGARSS会议)

[2] Z. Liu and L. Ma, “Class-wise adversarial transfer network for remote sensing scene classification,” IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, September 26 - October 2, 2020, DOI: 10.1109/IGARSS39084.2020.9323406. (研究生参加IGARSS会议)

[3] D. Shen and L. Ma, Cross-domain extreme learning machine for classification of hyperspectral images,” IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp.3305-3308, 2019. (研究生参加IGARSS会议)

[4] Chuang Luo, Li Ma*, Neighbor Consistency Based Unsupervised Manifold Alignment for Classification of Remote Sensing Images, the 10th International Workshop on Pattern Recognition in Remote Sensing, 2018, Beijing, China. (研究生参加PRRS会议)

[5] Jiazhen Song, Li Ma*, Reconstruction based Transfer Network for Classifiction of Remote Senisng Image, the 10th International Workshop on Pattern Recognition in Remote Sensing, 2018, Beijing, China. (研究生参加PRRS会议)

[6] Andong Ma, Li Ma*. Multi-feature based Label Propagation for Semi-supervised Classification of Hyperspectral Data. IEEE Workshop on Hyperspectral Image and Signal Processing-Evolution in Remote Sensing, Swtzerland, Laussane, 2014. (研究生参加Whispers会议)

[7] Xiaopan Wang, Li Ma*, Fujiang Liu. “Laplacian Support Vector Machine for Hyperspectral Image Classification by Using Manifold Learning Algorithms”. IEEE International Symposium on Geoscience and Remote Sensing, July, 1027,  Australia,Melbourne, 2013.(研究生参加IGARSS会议)

[8] Li Ma*, Melba M. Crawford, and Jinwen Tian. “Anomaly detection for hyperspectral images using local tangent space alignment”. IEEE International Symposium on Geoscience and Remote Sensing, July, 824, Honolulu, Hawaii, USA, 2010.


专著

[1] Melba M. Crawford, Li Ma, and W. Kim. Exploring nonlinear manifold learning for classification of hyperspectral data. Chapter 11 of Book “Optical Remote Sensing - Advances in Signal Processing and Exploitation Techniques”, S. Prasad, Ed. London, U.K.: Springer-Verlag, 2012, pp.207-234.


科研项目:

[1] 主持国家自然科学基金面上项目(61771437):基于动态联合图进行迁移学习的遥感图像分类方法研究,2018.1-2021.12

[2] 主持国家自然科学基金青年基金(61102104):基于流形学习算法进行图结构设计的高光谱图像分类技术研究,2012.1-2014.12

[3] 主持中科院光谱成像技术重点实验室开放基金(LSIT201702D):面向高光谱图像分类的无监督迁移学习算法研究,2017.4-2019.4

[4] 主持威尼斯欢乐娱人城3328摇篮计划高光谱遥感图像半监督分类算法,2012.1-2014.12

[5] 主持中央高校新青年教师资金项目高分辨率遥感影像分类技术研究,2011.11-2013.12.

[6] 参与国家自然科学基金重大研究计划(91442201):活体跨层次整合成像研究肿瘤肝转移的区域免疫,2015.1-2018.12

[7] 参与国家自然科学基金(编号:41101420):面向高空间分辨率遥感影像分割的图割方法2012.1-2014.12

[8] 参与中央高校优秀青年教师特色学科团队:多光谱集成仪器研发和环境监测应用研究, 2012.1-2014.12

发明专利:

[1]马丽;杨孝全;张晓锋;吴让仲;罗大鹏,基于局部流形学习构图的高光谱遥感图像半监督分类方法,专利号:201410651950.0.

[2]马丽,张晓锋,周群群,喻鑫,基于空间正则化流形学习算法的高光谱遥感图像分类方法,专利号:ZL2015 1 0515751.1.

[3] 马丽,祝蕾,一种用于遥感图像分类的基于类心对齐的迁移学习方法,专利号:ZL2015 1 0799789.6.

[4] 马丽,祝蕾,一种基于类心和协方差对齐的遥感图像迁移学习方法,专利号:ZL201710456531.5.


学术兼职:

IEEE Journal of Selected Topics in Applied Earth Observation and Remote SensingAssociate
Editor

IEEE Transactions on Geoscience and Remote SensingIEEE Geoscience and Remote Sensing Letters, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, International Journal of Remote Sensing等期刊审稿人。


本科生课程:

l 模式识别

l 神经网络与深度学习

l 人工智能导引和程序设计基础

l C++语言程序设计


获奖:

l 2019年,学校本科教学质量评价优秀(前10%);

l 2018年,学校本科教学质量评价优秀(前10%);

l 2017年,学校本科教学质量评价优秀(前10%);

l 2016年,年终考核校级优秀个人;

l 2015年,湖北省第十六届自然科学优秀学术论文二等奖;

l 2015年,年终考核校级优秀个人;

l 2015年,学校本科教学质量评价优秀(前10%);

l 2014年,学校第七届青年教师讲课比赛一等奖;

l 2012年,入选学校摇篮计划。


学生培养

硕士研究生

2012级:朱菲,鞠才

2013级:田银娇,鲁锦涛,邢晨

2014级:谢晓凤,祝蕾,吴东洋

2015级:郭倩倩

2016级:宋佳珍,罗闯

2017级:周黎,沈铎

2018级:刘子绪,王雯瑾,陈雪晴,陈敏

2019级:危红康,张海洋,欧江琳

2020级:李书悦;朱玲慧;王伟奇;曾铮

研究生发表学术论文:

l 2019级研究生危红康发表SCIT2)论文1会议论文1篇;

l 2018级研究生王雯瑾发表SCIT2)论文1会议论文1篇;

l 2018级研究生陈敏发表SCIT2)论文1

l 2018级研究生刘子绪发表SCIT2)论文1会议论文1篇;

l 2017级研究生周黎发表SCIT3)论文1获校级优秀硕士学位论文;

l 2016级研究生罗闯发表SCIT3)论文2获校级优秀硕士学位论文;

l 2016级研究生宋佳珍发表SCIT4)论文1会议论文1篇;

l 2014级研究生祝蕾发表SCIT2T3)论文2篇,获校级优秀硕士学位论文

l 2013级研究生邢晨发表SCIT4)论文1篇,获校级优秀硕士学位论文

学校优秀硕士论文:

l 周黎,基于异构迁移学习的高光谱遥感图像分类,2020

l 罗闯基于预测信息进行迁移学习的遥感图像分类算法,2019

l 祝蕾,基于无监督迁移学习算法的遥感图像分类,2017

l 邢晨,基于深度学习的高光谱遥感图像分类,2016

湖北省优秀学士论文:

l 邢晨,基于TLD方法的运动目标跟踪,2013.

l 刘小金,基于空间信息稀疏表示的高光谱图像分类,2015

l 宋佳珍,基于深度神经网络的高光谱遥感图像分类,2016

l 宋媛媛,基于标签对齐的高光谱遥感图像分类,2017

l 王雯瑾,基于低秩表达的迁移学习算法在遥感图像分类中的应用,2018