Hyperspectral unmixing consists in determining the reference spectral
signatures composing a hyperspectral image and their relative
abundance fractions in each pixel. In practice, the identified signatures
may be affected by a significant spectral variability resulting
for instance from the temporal evolution of the imaged scene. This
phenomenon can be accounted for by using a perturbed linear mixing
model. This paper studies an online estimation algorithm for the
parameters of this extended linear mixing model. This algorithm is
of interest for the practical applications where the size of the hyperspectral
images precludes the use of batch procedures. The performance
of the proposed method is evaluated on synthetic data.