Hyperspectral Source Separation

Hyperspectral cameras acquire electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. This enhanced spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. However, due to low spatial resolution of hyperspectral cameras, microscopic material mixing, and multiple scattering, the spectra measured by these cameras are mixtures of spectra of materials, called endmembers. Thus, accurate estimation requires some sort of spectral separation.

Spectral separation, or unmixing, is a blind source separation that involves estimating all or some of the of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and dataset size.

In this talk I will present an overview of unmixing methods with focus on geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms. The underlying mathematical problems and potential solutions are described.

José Bioucas

José Bioucas-Dias received the EE, MSc, PhD, and ``Agregado" degrees from Instituto Superior Técnico (IST), Technical University of Lisbon (TULisbon, now University of Lisbon), Portugal, in 1985, 1991, 1995, and 2007, respectively, all in electrical and computer engineering. Since 1995, he has been with the Department of Electrical and Computer Engineering, IST, where he was an Assistant Professor from 1995 to 2007 and an Associate Professor since 2007. Since 1993, he is also a Senior Researcher with the Pattern and Image Analysis group of the Instituto de Telecomunicações, which is a private non-profit research institution. His research interests include inverse problems, signal and image processing, pattern recognition, optimization, and remote sensing. Dr. Bioucas-Dias has authored or co-authored more than 250 scientific publications including more than 70 journal papers (48 of which published in IEEE journals) and 180 peer-reviewed international conference papers and book chapters.IT