学术报告

信电号角系列学术报告:Hyperspectral Imaging for Agriculture Applications

发布日期:2018-10-12 浏览次数:

报告人:Dr Jun Zhou,

School of Information and Communication Technology in Griffith UniversityAustralia

时  间:2018年10月12日  上午9: 30

地  点:信电四楼 406会议室

联系人:安冬教授 13366883406

Abstract:

Hyperspectral imagery contains rich information on the spectral and spatial distribution of object materials in a scene. Traditional hyperspectral remote sensing methods mainly focus on pixel level spectral analysis. On the contrary, computer vision has discovered color, texture, and various spatial and structural features of objects, but not spectral information. It is necessary to combine these two disciplines to more effectively explore the information beyond the visible spectrum. This talk gives an overview of hyperspectral imaging technology and how it can be used to address challenges in agricultural applications. Several case studies, including soil component analysis, early detection of plant pathogen, insect detection, and food quality analysis, will be covered in this talk.

 

Bio:Jun Zhou received the B.S. degree in Computer Science from Nanjing University of Science and Technology, China, in 1996, the M.S. degree in Computer Science from Concordia University, Canada in 2002, and the Ph.D. degree in computing science from the University of Alberta, Canada, in 2006. He joined the School of Information and Communication Technology in Griffith University in June 2012, where he is currently a senior lecturer. Prior to his appointment in Griffith University, he had been a research fellow in the Australian National University and a researcher at NICTA Canberra Lab. Dr Zhou was a winner of the Discovery Early Career Research Award from the Australian Research Council in 2012. He is a key member of the ARC Research Hub for Driving Farming Productivity and Disease Prevention which has been funded for more than 10 million Australian dollars over the next 5 years. His research interests are in pattern recognition, computer vision, hyperspectral imaging, and their applications to remote sensing, environmental informatics, and agriculture.


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