Home / Research / Hyperspectral imagery /

Comparing nonlinear mixing models for vegetated areas using simulated and real hyperspectral data

Joint work with L. Tits, B. Somers, Y. Altmann and P. Coppin

Most of the unmixing algorithms proposed in the literature rely 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, namely the Fan model, the Nascimento/Somers model, the generalized bilinear model and the polynomial post-nonlinear model (to have a brief overview of these models, the interested reader is invited to consult this paper). These physics-based 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.

The preliminary results are reported in the paper published in IEEE J. Sel. Topics Appl. Earth Observations Remote Sensing in 2014:

The datasets used in this study are publicly available online:

The Matlab codes for the unmixing algorithms based on the models under test are available here:

sitemeter stats