A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Wing-Kin Ma, Jose M. Bioucas-Dias, Tsung-Han Chan, Nicolas Gillis, Paul Gader, Antonio J. Plaza, ArulMurugan Ambikapathi, Chong-Yung Chi
2014 IEEE Signal Processing Magazine  
B lind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2] . Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]-[7],
more » ... by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing-blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts-discovered by early remote sensing researchers through empirical observations [3]-[7] and refined by later research-are elegant and very different from statistical independence-based BSS approaches established in [ Insights from remote sensing ] [ Wing
doi:10.1109/msp.2013.2279731 fatcat:yltzlzhbmbfthfw4qim4bj2tjy