(Minimum Volume Simplex Analysis for Hyperspectral Unmixing)
Spectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. It amounts at identifying a set of pure spectral signatures, called endmembers, and their corresponding fractional abundances in each pixel of the hyperspectral image. In this talk, we will present our recent developments including: minimum volume simplex analysis (MVSA) and collaborative nonnegative matrix factorization (R-CoNMF), which identify the endmembers and its corresponding fractions by finding the minimum volume simplex enclosing the data.
Jun Li received the B.S. degree in Geographic Information Systems from Hunan Normal University, Changsha, China, in 2004, the M.E. degree in Remote Sensing from Peking University, Beijing, China, in 2007, and the Ph.D. degree in Electrical Engineering from the Instituto de Telecomunicações, Instituto Superior Técnico (IST), Universidade Técnica de Lisboa, Lisbon, Portugal, in 2011.
From 2007 to 2011, she was a Marie Curie Research Fellow with the Departamento de Engenharia Electrotcnica e de Computadores and the Instituto de Telecomunicações, IST, Universidade Técnica de Lisboa, in the framework of the European Doctorate for Signal Processing (SIGNAL). She has also been actively involved in the Hyperspectral Imaging Network, a Marie Curie Research Training Network involving 15 partners in 12 countries and intended to foster research, training, and cooperation on hyperspectral imaging at the European level. Since 2011, she has been a Postdoctoral Researcher with the Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politécnica, University of Extremadura, Cáceres, Spain. Currently, she is a Professor with Sun Yat-Sen University, Guangzhou, China. Her research interests include hyperspectral image classification and segmentation, spectral unmixing, signal processing, and remote sensing. She is an Associate Editor for the IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing.