Ma Li (1982.3-), PhD, associate professor, master supervisor, visiting scholar of Purdue University from 2008 to 2010, PhD from Huazhong University of Science and Technology in 2010, visiting scholar of Mississippi State University in 2018, has presided over 1 General Program of National Natural Science Foundation of China, 1 Youth Program of National Natural Science Foundation of China and 1 Cradle Program of China University of Geosciences, and has participated in 2 Major Programs of National Natural Science Foundation of China. She is mainly engaged in the research on remote sensing image analysis, machine learning and deep learning, has published many academic papers in domestic and overseas important academic journals and conferences, and has been invited to write one chapter of a foreign book. Her papers have been cited for 557 times (statistics by Web of Science)
Contact information:
E-mail: maryparisster@gmail.com; 123378879@qq.com
Office: Room 523, No. 2 Teaching Building
Main experiences:
2011.3-present, Department of Communication Engineering, School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan)
2018.3-2018.8, Visiting scholar, Mississippi State University
2008.9-2010.9, Visiting scholar, Remote Sensing Application Laboratory, Purdue University
2006.9-2011.3, Doctor of Engineering in Pattern Recognition and Intelligent System, Institute of Image Recognition & Artificial Intelligence, Huazhong University of Science and Technology
2004.9-2006.6, Master of Engineering in Pattern Recognition and Intelligent System, School of Control Science and Engineering, Shandong University
2000.9-2004.6, Bachelor of Engineering in Biomedical Engineering, School of Control Science and Engineering, Shandong University
Main research fields:
1. Remote sensing image analysis
Hyperspectral remote sensing image classification; hyperspectral remote sensing image target detection; high-resolution remote sensing image processing
2. Machine learning algorithm
Manifold learning; transfer learning; sparse representation; deep learning
Publications:
Journal papers (first author and corresponding author (*)):
[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. 508–521, 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. 6236–6248, 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. 2305–2323, April 2019. (SCI, T2) IF=5.855 (by Web of Science)
[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. 1650–1654, 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. 1781–1785, 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=3.827 (by Web of Science)
[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=3.255 (by Web of Science)
[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=3.255 (by Web of Science)
[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. (by Web of Science)
[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. 2832–2844, 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) (cited)
[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) (cited)
[18] Yuanjie Shao, Guoping Wu, Li Ma*. Graph Based Semi-supervised Learning with Class-probability Distance for Hyperspectral Remote Sensing Image Classification. Acta Geodaetica et Cartographica Sinica, 2014, 43(11): 82-89. (EI)
[19] Li Ma*, Cai Ju, Fei Zhu. Anomaly detection oriented dimensionality reduction algorithm for hyperspectral images, Science of Surveying and Mapping, 2015, 39(7).
[20] Xiaopan Wang, Li Ma*, Fujinag Liu. A Weighted K-nearest Neighbor Algorithm Based on Linear Neighborhood Propagation, Computer Engineering, 2013, 39(7): 288-292.
[21] Li Ma*, Jinwen Tian. Anomaly Detection Algorithm for Hyperspectral Images Based on Local Energy Maximal Division, Journal of Remote Sensing, 2008, 12(3): 420-427.
[22] Li Ma*, Faliang Chang, Yizheng Qiao. Target Tracking Based on Mean Shift Algorithm and Particle Filtering Algorithm, Pattern Recognition and Artificial Intelligence, 2006,19,(6): 787-793. (EI)
Journal papers (co-author):
[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. (SCI, 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. (SCI, T2)
[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. (SCI)
[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. (SCI)
[5] Faliang Chang, Li Ma, Yizheng Qiao. Target Tracking Based on Adaptive Particle Filter under Complex Background. Acta Electronica Sinica, 2006, 34(12):2150-2153. (EI)
[6] Faliang Chang, Li Ma, Yizheng Qiao. Target Tracking Algorithm under Occlusion Based on Feature Correlation Matching, Journal of Image and Graphics, 2006, 11(6):817-822.
[7] Faliang Chang, Li Ma, Yizheng Qiao. Study on Vision Target Tracking Method under Occlusion, Control and Decision, 2006, 21(5): 503-507. (EI)
[8] Faliang Chang, Li Ma, Yizheng Qiao. Person Oriented Multi-object Tracking Algorithm in Video Sequence, Control and Decision, 2007, 22(4):418-422. (EI)
Conference papers (first author and corresponding author):
[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. (Graduate student attending IGARSS conference)
[2] [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. (Graduate student attending IGARSS conference)
[3] [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. (Graduate student attending IGARSS conference)
[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. (Graduate student attending PRRS conference)
[5] Jiazhen Song, Li Ma*, Reconstruction based Transfer Network for Classification of Remote Sensing Image, the 10th International Workshop on Pattern Recognition in Remote Sensing, 2018, Beijing, China. (Graduate student attending PRRS conference)
[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. (Graduate student attending Whispers conference)
[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. (Graduate student attending IGARSS conference)
[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.
Monographs:
[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.
Scientific research programs:
[1] General Program of National Natural Science Foundation of China (61771437): Research on remote sensing image classification method based on dynamic joint image for transfer learning, 2018.1-2021.12, PI
[2] Youth Program of National Natural Science Foundation of China (61102104): Research on the hyperspectral image classification technique based on manifold learning algorithm for image structure design, 2012.1-2014.12, PI
[3] Open fund of the Key Lab of spectral imaging technology, Chinese Academy of Sciences (LSIT201702D): Research on unsupervised transfer learning algorithm for hyperspectral image classification, 2017.4-2019.4, PI
[4] Cradle Program of China University of Geosciences (Wuhan): Semi-supervised classification algorithm for hyperspectral remote sensing images, 2012.1-2014.12, PI
[5] Program supported by the Foundation for New Youth Scholars in Central Universities: Research on high-resolution remote sensing images classification technology, 2011.11-2013.12, PI
[6] Major Program of National Natural Science Foundation of China (91442201): Study on the regional immunity of liver metastasis by in vivo cross-level integrated imaging, 2015.1-2018.12, participant
[7] National Natural Science Foundation of China (No.: 41101420): Image segmentation method for high spatial resolution remote sensing images, 2012.1-2014.12, participant
[8] Featured discipline team composed of outstanding young scholars from central universities: Development of multispectral integrated instrument and research on the application in environmental monitoring, 2012.1-2014.12, participant
Invention patents:
[1] Li Ma, Xiaoquan Yang, Xiaofeng Zhang, Rangzhong Wu, Dapeng Luo, Semi-supervised hyperspectral remote sensing images classification based on local manifold learning composition, Patent No.: 201410651950.0
[2] Li Ma, Xiaofeng Zhang, Qunqun Zhou, Xin Yu, Hyperspectral remote sensing images classification method based on spatial regularized manifold learning algorithm, Patent No.: ZL2015 1 0515751.1
[3] Li Ma, Lei Zhu, A transfer learning method based on centered alignment for remote sensing images classification, Patent No.: ZL2015 1 0799789.6
[4] Li Ma, Lei Zhu, A Remote Sensing Image Migration Learning Method Based on Class Mind and Covariance Alignment, Patent No.: ZL201710456531.5
Concurrent academic posts:
Associate Editor of IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing
Reviewer of IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, International Journal of Remote Sensing and other journals.
Undergraduate courses:
l Pattern Recognition
l Neural network and deep learning
l Fundamentals of Artificial Intelligence Guidance and Programming
l C++ Language Programming
Awards:
l 2019, Excellence Award in Undergraduate Teaching Quality Evaluation (Top 10%);
l 2018, Excellence Award in Undergraduate Teaching Quality Evaluation (Top 10%);
l 2017, Excellence Award in Undergraduate Teaching Quality Evaluation (Top 10%);
l 2016, Outstanding individual in year-end assessment of CUG;
l 2015, Second Prize in the 16th Natural Science Excellent Academic Papers of Hubei Province;
l 2015, Outstanding individual in year-end assessment of CUG;
l 2015, Excellence Award in Undergraduate Teaching Quality Evaluation (Top 10%);
l 2014, First Prize in the 7th Young Teachers Teaching Competition of the University;
l 2012, Selected in the Cradle Program of CUG.
Students Training:
Postgraduate students:
Grade 2012: Fei Zhu, Cai Ju
Grade 2013: Yinjiao Tian, Jintao Lu, Chen Xing
Grade 2014: Xiaofeng Xie, Lei Zhu, Dongyang Wu
Grade 2015: Qianqian Guo
Grade 2016: Jiazhen Song, Chuang Luo
Grade 2017: Li Zhou, Duo Shen
Grade 2018: Zixu Liu, Wenjin Wang, Xueqing Chen, Min Chen
Grade 2019: Hongkang Wei, Haiyang Zhang, Jianglin, Ou
Grade 2020: Shurui Li, Linghui Zhu, Weiqi Wang, Zheng Zeng
Academic papers published by postgraduates:
l Hongkang Wei, postgraduate of Grade 2019, published 1 SCI (T2) paper and 1 conference paper;
l Wenjin Wang, postgraduate of Grade 2018, published 1 SCI (T2) paper and 1 conference paper;
l Li Zhou, postgraduate of Grade 2017, published one SCI (T3) paper;
l Chuang Luo, postgraduate of Grade 2016, published two SCI (T3) papers;
l Jiazhen Song, postgraduate of Grade 2016, published one SCI (T4) paper;
l Lei Zhu, postgraduate of Grade 2014, published two SCI (T2, T3) papers and was awarded excellent master’s thesis of CUG;
l Chen Xing, postgraduate of Grade 2013, published one SCI (T4) paper and was awarded excellent master’s thesis of CUG;
Excellent master’s theses of CUG:
l Li ZHOU, Hyperspectral remote sensing image classification based on heterogeneous migration learning, 2020
l Chuang LUO, Remote sensing image classification algorithm based on transfer learning of prediction information, 2019
l Chen Xing, Hyperspectral remote sensing image classification based on deep learning, 2016
l Lei Zhu, Remote sensing image classification based on unsupervised transfer learning algorithm, 2017
Excellent bachelor’s thesis of Hubei Province:
l Chen Xing, Moving object tracking based on TLD, 2013.
l Xiaojin Liu, Hyperspectral image classification based on sparse representation of spatial information, 2015
l Jiazhen Song, Hyperspectral remote sensing image classification based on deep neural network, 2016
l Yuanyuan Song, Hyperspectral remote sensing image classification based on label alignment, 2017
l Wenjin Wang, Application of transfer learning algorithm in remote sensing image classification based on low-rank representation, 2018
