Time: 14:30-17:00 Satursday 7/8/2017
Place：Room 218 Yi Building old campus
Invited guest：School of Software and Microelectronics
The first report:
Topic：Advanced techniques for feature extraction and data analysis in hyperspectral imaging
Reporter: Jinchang Ren
Brief introduction to the report：Although hyperspectral imaging has been widely applied in a number of application areas such as remote sensing, precision agriculture, mining and surveillance, food/drink inspection, pharmaceutical, material, and security. One fundamental problem is feature extraction from the hypercube, which has severely constrained its applicability. In this talk, several key techniques for feature extraction in hyperspectral imaging are reported, including structured PCA, folded-PCA and singular spectrum analysis. Experimental results on several remote sensing datasets are presented to show the efficacy of these techniques.
The second report
Topic：Changing Arctic sea ice: the emergence of a dominant Marginal Ice Zone
Reporter：Phil (Byongjun) Hwang
Brief introduction to the report：The rapid decline of Arctic sea ice during the past decades is a conspicuous indicator of climate change. This reduced sea ice cover is brining profound changes to the Arctic physical and bio-geo-chemical environments. Satellite observations show that the Marginal Ice Zone (MIZ), a region of low ice concentration area consisting of a relatively disperse collection of small ice floes, has grown. Model projections indicate a growth of the MIZ from 10% to 80% of the summer sea ice cover by 2050. With this emerging MIZ, surface ocean wave increases; sea ice breaks up into smaller floes; deformation of sea ice intensifies; air-ocean momentum transfer increases; upper ocean easily heats up by sun. Current climate models do not have proper parameterizations to describe MIZ processes, which makes it difficult to predict future Arctic sea ice and its cascading effects. In this talk, I present recent research activities on MIZ processes using satellite and autonomous observing technologies. These technologies provide better understanding of MIZ processes and provide the data to calibrate/validate MIZ-related parameterizations in the models.