The spectral signatures of the materials contained in hyperspectral images, also called
endmembers (EMs), can be significantly affected by variations in atmospheric, illumination …

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 …

Over the past decades, enormous efforts have been made to improve the performance of
linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to …

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …

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 …

The composite nature of remotely sensed spectral information often masks diagnostic
spectral features and hampers the detailed identification and map** of targeted …

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 …

Nonlinear models have recently shown interesting properties for spectral unmixing. This
paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for …

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

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 …