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- José M. P. Nascimento, José M. Bioucas-Dias
- IEEE Trans. Geoscience and Remote Sensing
- 2005

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data,… (More)

- Manya V. Afonso, José M. Bioucas-Dias, Mário A. T. Figueiredo
- IEEE Transactions on Image Processing
- 2010

We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an <i>l</i><sub>2</sub> data-fidelity term and a nonsmooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based… (More)

- José M. Bioucas-Dias, Mário A. T. Figueiredo
- IEEE Transactions on Image Processing
- 2007

Iterative shrinkage/thresholding (1ST) algorithms have been recently proposed to handle a class of convex unconstrained optimization problems arising in image restoration and other linear inverse problems. This class of problems results from combining a linear observation model with a nonquadratic regularizer (e.g., total variation or wavelet-based… (More)

- Manya V. Afonso, José M. Bioucas-Dias, Mário A. T. Figueiredo
- IEEE Transactions on Image Processing
- 2011

We propose a new fast algorithm for solving one of the standard approaches to ill-posed linear inverse problems (IPLIP), where a (possibly nonsmooth) regularizer is minimized under the constraint that the solution explains the observations sufficiently well. Although the regularizer and constraint are usually convex, several particular features of these… (More)

—Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via… (More)

- Jun Li, José M. Bioucas-Dias
- IGARSS
- 2008

This paper presents a new method of minimum volume class for hyperspectral unmixing, termed minimum volume simplex analysis (MVSA). The underlying mixing model is linear; i.e., the mixed hyperspectral vectors are modeled by a linear mixture of the end-member signatures weighted by the correspondent abundance fractions. MVSA approaches hyperspectral unmixing… (More)

- Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio J. Plaza
- IEEE Trans. Geoscience and Remote Sensing
- 2011

Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by an imaging spectrometer. In many situations, the identification of endmember signatures in the original data set may be… (More)

- Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio J. Plaza
- IEEE Trans. Geoscience and Remote Sensing
- 2012

—Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the… (More)

- José M. Bioucas-Dias, Gonçalo Valadão
- IEEE Transactions on Image Processing
- 2005

Phase unwrapping is the inference of absolute phase from modulo-2pi phase. This paper introduces a new energy minimization framework for phase unwrapping. The considered objective functions are first-order Markov random fields. We provide an exact energy minimization algorithm, whenever the corresponding clique potentials are convex, namely for the phase… (More)

- José M. Bioucas-Dias
- 2009 First Workshop on Hyperspectral Image and…
- 2009

This paper presents a new linear hyperspectral unmixing method of the minimum volume class, termed simplex identification via split augmented Lagrangian (SISAL). Following Craig's seminal ideas, hyperspectral linear unmixing amounts to finding the minimum volume simplex containing the hyperspectral vectors. This is a nonconvex optimization problem with… (More)