  Spectral unmixing 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 mixing model to describe the observed pixels.
Unfortunately, this model has been shown to be of limited interest for specific
scenes, in particular when acquired over vegetated areas. Consequently, in the
past few years, several nonlinear mixing models have been introduced to take
nonlinear effects into account while performing spectral unmixing. These models
have been proposed empirically, however without any thorough validation. In
this paper, the authors take advantage of two sets of real and physical-based
simulated data to validate the accuracy of various nonlinear models in
vegetated areas. These physics-based and analysis models, and their
corresponding unmixing algorithms, are evaluated with respect to their ability
of fitting the measured spectra and of providing an accurate estimation of the
abundance coefficients, considered as the spatial distribution of the materials
in each pixel.
