【百家大講堂】第288期： 光譜解混與端元可變性研究 

　　講座題目：光譜解混與端元可變性研究 

　　報  告 人：Jocelyn Chanussot 

　　時  　 間：2019年11月29日 下午15:00-17:00

　　地　   點：中關村校區(qū)10號教學樓205

　　主辦單位：研究生院,、 信息與電子學院

　　報名方式：登錄北京理工大學微信企業(yè)號---第二課堂---課程報名中選擇“【百家大講堂】第288期：光譜解混與端元可變性研究  ”

 

【主講人簡介】

 Jocelyn Chanussot,，法國格勒諾布爾理工學院教授,。 長期從事于圖像分析,，數據融合,，機器學習以及人工智能在遙感領域應用等研究?，F任IEEE地球科學與遙感學會副主席,，負責協(xié)會會議組織相關工作,。擔任IEEE T-GRS雜志與IEEE T-IP雜志副主編,，從2011年到2015年,，曾任 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 雜志主編,。 發(fā)表國際期刊論文160余篇， 多次獲得相關國際學術獎勵,。 2012年當選美國IEEE會士,， 2018、2019年兩次入選湯森路透社高被引科學家,。 

 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).

 

【講座信息】

光譜解混用于復原圖像中物質的純凈光譜,，是高光譜成像中一項重要的逆問題。線性解混模型通常應用于現有光譜解混研究,，并假設物質與光譜存在一一對應關系,。然而，在實際應用中,，此類假設會產生嚴重的光譜類間變異性問題,。因此，需要在光譜解混中允許光譜端元存在變化以達到更加魯棒的解混效果,。本次講座回顧現有針對端元變異問題的研究,，并對其分類，且在數據集進行測試分析,，以驗證端元變異問題對光譜解混的影響,。此項工作由Lucas Drumetz在其博士期間研究完成。

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.