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【百家大课堂】第288期: 光谱解混与端元可变性研讨

编辑: 研讨生院 公布日期: 2019-11-26 阅读量:
讲座标题:光谱解混与端元可变性研讨 
报 告 人:Jocelyn Chanussot 
时   间:2019年11月29日 下战书15:00-17:00
地  点:中关村学区10号讲授楼205
主理单元:研讨生院、 信息与电子学院
报名方法:登录北京理工大学微信企业号---第二讲堂---课程报名中选择“【百家大课堂】第288期:光谱解混与端元可变性研讨  ”
 
【主讲人简介】
 Jocelyn Chanussot,法国格勒诺布尔理工学院传授。临时从事于图像剖析,数据交融,呆板学习以及人工智能在遥感范畴使用等研讨。现任IEEE地球迷信与遥感学会副主席,担任协会集会构造相干任务。担当IEEE T-GRS杂志与IEEE T-IP杂志副主编,从2011年到2015年,曾任 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 杂志主编。 宣布国际期刊论文160余篇, 屡次取得相干国际学术嘉奖。 2012年中选美国IEEE会士, 2018、2019年两次当选汤森路透社高被引迷信家。 
 
Jocelyn Chanussot is currently a Professor of signal and image processing at the Grenoble Institute of Technology, France. His research interests include image analysis, data fusion, machine learning and artificial intelligence in remote sensing. Dr. Chanussot is the Vice President of the IEEE Geoscience and Remote Sensing Society, in charge of meetings and symposia. He is an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING and the IEEE TRANSACTIONS ON IMAGE PROCESSING. He was the Editor-in-Chief of the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING from 2011 to 2015. He is the co-author of over 165 papers in international journals and has received several scientific awards and recognitions.  He is a Fellow of the IEEE (2012) and a Highly Cited Researcher (Clarivate Analytics/Thomson Reuters, 2018, 2019).
 
【讲座信息】
  光谱解混用于恢复图像中物质的纯洁光谱,是高光谱成像中一项紧张的逆题目。线性解混模子通常使用于现有光谱解混研讨,并假定物质与光谱存在逐个对应干系。但是,在实践使用中,此类假定会发生严峻的光谱类间变异性题目。因而,需求在光谱解混中容许光谱端元存在变革以到达愈加鲁棒的解混结果。本次讲座回忆现有针对端元变异题目的研讨,并对其分类,且在数据集停止测试剖析,以验证端元变异题目对光谱解混的影响。此项任务由Lucas Drumetz在其博士时期研讨完成。
 
Spectral Unmixing is an inverse problem in hyperspectral imaging which aims at recovering the spectra of the pure constituents of an image (called endmembers), as well as at estimating the proportions of said materials in each pixel (called abundances). A linear mixing model is typically used for this purpose, but this approach implicitly assumes that one spectrum can completely characterize each material, while in practice they are always subject to intra-class variability. Taking this phenomenon into account within an image amounts to allowing the endmembers to vary on a per-pixel basis. In this talk, we review and categorize the recent methods addressing this endmember variability and compare their results on a real dataset, thus showing the benefits of incorporating it in the unmixing chain. The work was conducted by Lucas Drumetz during his PhD.
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