  This paper presents a nonlinear mixing model for joint hyperspectral image
unmixing and nonlinearity detection. The proposed model assumes that the pixel
reflectances are linear combinations of known pure spectral components
corrupted by an additional nonlinear term, affecting the endmembers and
contaminated by an additive Gaussian noise. A Markov random field is considered
for nonlinearity detection based on the spatial structure of the nonlinear
terms. The observed image is segmented into regions where nonlinear terms, if
present, share similar statistical properties. A Bayesian algorithm is proposed
to estimate the parameters involved in the model yielding a joint nonlinear
unmixing and nonlinearity detection algorithm. The performance of the proposed
strategy is first evaluated on synthetic data. Simulations conducted with real
data show the accuracy of the proposed unmixing and nonlinearity detection
strategy for the analysis of hyperspectral images.
