The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the mailing list webpage. Anyone can attend the seminars; no registration is necessary. If you would like to present something, please send us an email.

The seminars are usually held every other Tuesday, from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Tuesday, January 6th 2015, 13h00 - 14h00

José Bioucas-Dias (IT)

Hyperspectral Source Separation

Anfiteatro do Complexo Interdisciplinar

Instituto Superior Técnico - Alameda


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.


Bio: 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.