Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

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 a technique for remotely sensed image interpretation that expresses
each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and …

Data acquired from multichannel sensors are a highly valuable asset to interpret the
environment for a variety of remote sensing applications. However, low spatial resolution is …

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference
spectral signatures composing the data-referred to as endmembers-their abundance …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Spectral unmixing is an important task in hyperspectral image processing for separating the
mixed spectral data pertaining to various materials observed aiming at analyzing the …

Hyperspectral unmixing is an important processing step for many hyperspectral applications,
mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation …

Hyperspectral unmixing is an important problem for remotely sensed data interpretation. It
amounts at estimating the spectral signatures of the pure spectral constituents in the scene …