Machine Learning Techniques for Remote Sensing

The machine learning techniques can be roughly categorized into those involving unsupervised learning, supervised learning, semi-supervised learning, reinforcement learning, and meta learning. The talk will discuss the challenges in various remote sensing fields and what category of machine learning techniques can be developed for addressing these challenges. Specifically, the talk will introduce an unsupervised learning technique of divergent subsets for mixed pixel decomposition, a supervised learning technique of cloudy image arithmetic based methods for cloud removal, a semi-supervised learning technique of mutual guide for hyperspectral image classification, a reinforcement learning technique of basic method composite for underwater image enhancement, and a meta learning technique of meta captioning for remote sensing image captioning. The talk will be concluded by summarizing the properties of these new techniques.

Prof. Peng Ren received the BEng and MEng degrees both in electronic engineering from Harbin Institute of Technology, China, and PhD in Computer Science from the University of York, UK. He is currently a full professor with the College of Oceanography and Space Informatics, China University of Petroleum (East China). He is the director of Shandong Youth Innovative Team of offshore unmanned observation and also the director of Qingdao International Research Center for Intelligent Forecast and Detection of Oceanic Catastrophes. He received the K. M. Scott Prize from the University of York, the Natural Science award (first rank) from China Institute of Electronics, and the Eduardo Caianiello Best Student Paper Award from 18th International Conference on Image Analysis and Processing as one co-author. He is an associate editor of IEEE Journal on Miniaturization for Air and Space Systems and an editor of International Journal of Micro Air Vehicles. His research interests include remote sensing with machine learning, unmanned vehicle observation, and on-orbit FPGA computation, etc.

To register, please click here. Registration will close: 9 June 2022 at 09:00 (BST).

For further information or any queries, please contact the IDSAI Team.