  This paper presents an unsupervised algorithm for nonlinear unmixing of
hyperspectral images. The proposed model assumes that the pixel reflectances
result from a nonlinear function of the abundance vectors associated with the
pure spectral components. We assume that the spectral signatures of the pure
components and the nonlinear function are unknown. The first step of the
proposed method consists of the Bayesian estimation of the abundance vectors
for all the image pixels and the nonlinear function relating the abundance
vectors to the observations. The endmembers are subsequently estimated using
Gaussian process regression. The performance of the unmixing strategy is
evaluated with simulations conducted on synthetic and real data.
