Abstract:

Hyperspectral unmixing (HU) is one of the most prominent research topics in hyperspectral imaging in remote sensing. HU aims at identifying the underlying materials and their corresponding compositions in the scene, using the high spectral degrees of freedom of hyperspectral images. In this talk we will look at what are the key insights of HU from a signal processing perspective, how such insights lead to a unique branch of theory and methods for structured matrix factorization, and why HU has strong connections to problems from other areas such as machine learning, data analytics, computer vision and biomedical imaging. 

Biography:

Wing-Kin (Ken) Ma(IEEE Fellow) is an Associate Professor with the Department of Electronic Engineering, The Chinese University of Hong Kong. His research interests are in signal processing, communications and optimization, with recent activities focused on MIMO transceiver designs and interference management, and structured matrix factorization and applications. Dr. Ma is currently a Senior Area Editor of IEEE Transactions on Signal Processing and previously served as editors of several journals, e.g., IEEE Signal Processing Magazine as the Lead Guest Editor of a special issue. He is currently a member of the Signal Processing for Communications and Networking (SPCOM) Technical Committee. He received Research Excellence Award 2013–2014 by CUHK, the 2015 IEEE Signal Processing Magazine Best Paper Award, and the 2016 IEEE Signal Processing Letters Best Paper Award. His students received ICASSP Best Student Paper Awards in 2011 and 2014. He is an IEEE Fellow. He is currently an IEEE SPS Distinguished Lecturer.