Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …

Spectral unmixing is an important technique for remotely sensed hyperspectral data
exploitation. It amounts to identifying a set of pure spectral signatures, which are called …

We present a new algorithm for linear spectral mixture analysis, which is capable of
supervised unmixing of hyperspectral data while respecting the constraints on the …

Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing
(SU), which aims at obtaining the endmembers and corresponding fractional abundances …

This paper considers the problem of unsupervised spectral unmixing for hyperspectral
image analysis. Each observed pixel is assumed to be a noisy linear mixture of pure …

Nonnegative matrix factorization (NMF) with minimum-volume-constraint (MVC) is exploited
in this paper. Our results show that MVC can actually improve the sparseness of the results …

In the LMM for hyperspectral images, all the image spectra lie on a high-dimensional
simplex with corners called endmembers. Given a set of endmembers, the standard …

In hyperspectral imaging, spectral unmixing is one of the most challenging and fundamental
problems. It consists of breaking down the spectrum of a mixed pixel into a set of pure …

Need for a priori knowledge of the components comprising each pixel in a scene has set the
endmember determination, rather than the endmember abundance quantification, as the …

ALMOST A DECADE after the milestone special issue of the IEEE TRANSACTIONS ON
GEOSCIENCE AND REMOTE SENSING (TGRS) dedicated to the analysis of hyperspectral …