CAO Yungang

Head of the Department of Surveying and Geo-Informatics

Associate Professor for Photogrammetry and Remote Sensing

Research Interests

My research has focused on machine learning for Remote Sensing, and the application of Remote Sensing in the Natural Resources and Environment field. My group has developed the technologies for deep understanding of remote sensing images, remote sensing change detection and target recognition, spatial-temporal spectral information fusion and analysis, remote sensing investigation and evaluation of natural resources, and remote sensing monitoring and early warning and evaluation of natural disasters.

Connect

SWJTU XIPU Campus Building 4 Room 4139

Email: yungang@swjtu.cn

Education

Surveying Engineering, Southwest Jiaotong University, China 2000

MA.Eng. Geodesy and Survey Engineering, Southwest Jiaotong University, China 2003

Ph.D. Cartography and Geographical Information System, Chinese Academy of Sciences (CAS), China 2006

Experience

Associate Professor for Photogrammetry and Remote Sensing, Southwest Jiaotong University since 2013

Head of the Department of Surveying and Geo-Informatics at the Southwest Jiaotong University since 2020

Selected Publications

Xie, Y., Zhu, J.,Cao, Y.*, Feng, D., Hu, M., Li, W., ... & Fu, L. (2020). Refined Extraction Of Building Outlines From High-Resolution Remote Sensing Imagery Based on a Multifeature Convolutional Neural Network and Morphological Filtering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1842-1855.

Xie, Y., Zhu, J.,Cao, Y.*, Zhang, Y., Feng, D., Zhang, Y., & Chen, M. (2020). Efficient Video Fire Detection Exploiting Motion-Flicker-Based Dynamic Features and Deep Static Features. IEEE Access, 8, 81904-81917.

Li, W., Zhu, J., Zhang, Y., Fu, L., Gong, Y., Hu, Y., &Cao, Y*. (2020). An on-demand construction method of disaster scenes for multilevel users. Natural Hazards, 101(2), 409-428.

Zhang, M.,Cao, Y.*, Yang, X., Chen, K., Pan, M., & Guo, J. (2020). Accuracy Analysis of High Spatiotemporal Resolution NDVI Reconstruction Model in Grassland. Geography and Geo-Information Science,36(01):35-43.

Li, W., Zhu, J., Zhang, Y.,Cao, Y.*, Hu, Y., Fu, L., ... & Xu, B. (2019). A fusion visualization method for disaster information based on self-explanatory symbols and photorealistic scene cooperation. ISPRS International Journal of Geo-Information, 8(3), 104.

Yungang, C. A. O., Zhipan, W. A. N. G., Li, S. H. E. N., Xue, X. I. A. O., & Lei, Y. A. N. G. (2016). Fusion of pixel-based and object-based features for road centerline extraction from high-resolution satellite imagery. Acta Geodaetica et Cartographica Sinica, 45(10), 1231.