Lecture Topic:

Research on Spectral Unmixing and Endmember Variability

Lecturer:

Jocelyn Chanussot

Time:

November 29, 2019 (Friday) 15:00-17:00

Place:

Room 205, No.10 Teaching Building, Zhongguancun Campus

Organizer:

Graduate School, School of Information and Electronics

Registration:

Log-in to WeChat enterprise of Beijing Institute of Technology— 第二课堂（The Second Lecture）— Choose No.288 in the Lecture Registration

【Introduction to the lecturer】

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).

【Lecture Information】

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.