In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …

When considering the problem of unmixing hyperspectral images, most of the literature in
the geoscience and image processing areas relies on the widely used linear mixing model …

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 …

We introduce a robust mixing model to describe hyperspectral data resulting from the
mixture of several pure spectral signatures. The new model extends the commonly used …

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 …

Mainly due to the limited spatial resolution of the data acquisition devices, hyperspectral
image pixels generally result from the mixture of several components that are present in the …

The remote map** of minerals and discrimination of ore and waste on surfaces are
important tasks for geological applications such as those in mining. Such tasks have …

This paper presents a nonlinear mixing model for hyperspectral image unmixing. The
proposed model assumes that the pixel reflectances are post-nonlinear functions of …

Airborne and spaceborne hyperspectral sensors, due to their limited spatial resolution, often
record the spectral response of a mixture of materials. In order to extract the abundances of …

Spectral unmixing (SU) is a crucial processing step when analyzing hyperspectral data. In
such analysis, most of the work in the literature relies on the widely acknowledged linear …