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Hyperspectral Imaging

点击[] 时间[ 2016年07月12日 16:52] 发布人[王凌沛]

题目:Hyperspectral Imaging

报告时间:7月13日(下午)2:30

报告地点:新主楼G1039

主办单位:计算机学院

主讲人:Dr. Jun Zhou

Jun Zhou is a Reader at Griffith University, Australia. He started the position in the School of Information and Communication Technology in Griffith University from June 2012. At the same time, he hold an adjunct visiting fellow position from the Research School of Computer Science (RSCS), in the Australian National University (ANU). Before joining Griffith, he had been a research fellow in RSCS, ANU and a researcher in the Canberra research lab of NICTA. He had also held an adjunct position in the School of Engineering and Information Technology at the University of New South Wales.

He completed his PhD studies in Computing Science at the University of Alberta, Edmonton, Canada in 2006. From 1999 to 2002, he studied in the Department of Computer Science, Concordia University, Montreal,Canada. He also worked in the Centre for Pattern Recognition and Machine Intelligence (CENPARMI) as a research assistant and technical staff. He got his bachelor's degree from Nanjing University of Science and Technology, Nanjing, China. Jun Zhou was a winner of the Discovery Early Career Researcher Award (DECRA) from the Australian Research Council in 2011.

报告摘要:

Hyperspectral Image played important role in the area of Remote Sensing, Environment Monitoring, Surveillance and etc. In this report, we focus on the application of hyperspectral image in remote sensing object detection, classification and change detection. We develop new methods on semi-supervised based hyperspectral image analysis. We introduce a novel approach on how to combine spectral and spatial information from hyperspectral images. By doing so, we could use few labeled data for the tasks such as detection and classification. We could also improve the efficiency and accuracy on hyperspectral image analysis. In this talk, we will also introduce Dr. Jun Zhou’s recent research on the application of hyperspectral on close range target analysis.

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