Dr. Shou Feng, Associate Professor

College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

Biography

Dr. Shou Feng, Associate Professor, PhD Supervisor of Harbin Engineering University, Deputy Director of the Key Laboratory of Advanced Ship Communication and Information Technology of the Ministry of Industry and Information Technology, IEEE member, Senior member of the Chinese Society of Communications, Member of the Imaging Detection and Perception Committee of the Chinese Society of Image and Graphics, peer review expert of the National Natural Science Foundation of China, academic dissertation review expert of the Ministry of Education, Visiting Scholar of Indiana University Bloomington, Guest Editor of Remote Sensing, an international authoritative journal in the field of remote sensing. Member of the editorial board of international Journal Frontiers in Imaging, American Journal of Remote Sensing, He also serves as a reviewer for many authoritative academic journals such as IEEE TIP, IEEE TGRS, IEEE GRSL, and Remote Sensing. In the past three years, he has published 30 academic papers as the first/corresponding author in top journals in the field of Remote Sensing such as IEEE TIP, IEEE TGRS, and Remote Sensing, and 3 papers have been selected as ESI highly cited papers. As a guest editor, he organized 4 special issues in Remote Sensing, TOP journal of Chinese Academy of Sciences.

Topic

Deep Learning Methods for Hyperspectral Image Classification

Abstract

Remote sensing technology is an important technical means for human beings to perceive the world, and hyperspectral image classification technology has become the mainstream of current research. Hyperspectral image classification (HSIC) is a pixel-level classification task, which is mainly used for fine extraction and recognition of ground object information. HSIC is the basis for subsequent practical application tasks of hyperspectral images and has very important research significance, which is widely used in digital precision agriculture, environmental monitoring, national defense and military strategy and other fields. With the rapid development of artificial intelligence technology, many new hyperspectral image classification methods and algorithms have been proposed. Moreover, rapid advances in these methods have also promoted the application of associated algorithms and techniques to problems in many related fields. This keynote aims to report and cover the latest advances and trends about the Deep Learning Methods for Hyperspectral Image Classification.

Dr. Mingxin Liu, Professor

State Key Laboratory of Applied Organic Chemistry, Lanzhou University, Lanzhou, China

Biography

Dr. Mingxin Liu is a professor at Lanzhou University. His B.Sc. thesis was fulfilled from Tsinghua University with Prof. Yongge Wei and Prof. Lei Liu. He then finished his PhD at McGill University supervised by Prof. Chao-Jun Li. After finishing his PhD he completed his postdoctoral study with Prof. Zetian Mi from the University of Michigan. He began his independent career as a full professor at Lanzhou University by the end of 2020. His research interest is the application of 'green' reagents in synthetic methodology and their sustainable application in Chemical Biology.

Topic

Tuning 'Green' Feedstock into Value-added C-C Bonds Under Light

Abstract

The construction of C-C bonds is at the heart of Chemistry as well as sustainable development. The scale and diversity requirement of those processes in modern society is enormous. However, the traditional C-C bond formation process often requires metallic or pre-synthesized 'mediators', which reduces the overall atom economy and generate stoichiometric amount of emissions. We have developed a series of photocatalytic C-C bond formations, granting value-added compounds while using more environmentally-benign reagents, which has further broaderned the utilization of sustainable resource and allows 'green fuels' to be converted into 'green foods/materials'.