SHEN Li

Intelligent interpretation of remote sensing image, remote sensing for environmental applications

SHEN Li

Associate Professor of Surveying and Geo-Informatics

Research Interests

Intelligent interpretation of remote sensing image, remote sensing for environmental applications

Connect

SWJTU XIPU Campus Building 4 Room 4502

email:lishen@swjtu.edu.com

Education

(1) D.Sc, Cartography and Geography Information System, Beijing Normal University, 2008.9 - 2013.9

(2) Visiting Student, Image Processing, University of Waterloo, 2011.9 - 2012.9

(3) B.S, Remote Sensing Science and Technology, Wuhan University, 2004.9 - 2008.6

Experience

Faculty of Geosciences and Environmental Engineering at the Southwest Jiaotong University, Jan. 2014-Current

Research Interests

My research has focused on pattern recognition and machine learning theory and algorithms to make an optimal decision such as identifying or classifying objects in remote sensing (RS) imageries for various applications.

Selected Publications

[1]Jicheng Wang,Li Shen*, Wenfan Qiao, Yanshuai Dai, Zhilin Li. Deep feature fusion with integration of residual connection and attention model for classification of VHR remote sensing images. Remote Sensing, 2019, 11(13), 1617.

[2]Li Shen*, Linmei Wu, Yanshuai Dai, Wenfan Qiao, Ying Wang. Topic modelling for object-based unsupervised classification of VHR panchromatic satellite images based on multiscale image segmentation. Remote Sensing, 2017, 9(8): 840.

[3]Yuanxin Ye,Li Shen*, Ming Hao, Zhu Xu. Robust optical-to-SAR image matching based on shape properties. IEEE Geoscience and Remote Sensing Letters, 2017, 14(4): 564- 568.

[4] Li Shen, Hong Tang, Yunhao Chen, Adu Gong, Jing Li, Wenbin Yi. A semisupervised latent Dirichlet allocation model for object-based classification of VHR panchromatic satellite images. IEEE Geoscience and Remote Sensing Letters, 2014, 4(11):863-867.

[5] Hong Tang,Li Shen, Yinfeng Qi, Yunhao Chen, Yang Shu, Jing Li, David A. Clausi. A multiscale latent Dirichlet allocation model for object-oriented clustering of VHR panchromatic satellite images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3):1680-1692.