  In this work, we tackle the problem of hyperspectral (HS) unmixing by
departing from the usual linear model and focusing on a Linear-Quadratic (LQ)
one. The proposed algorithm, referred to as Successive Nonnegative Projection
Algorithm for Linear Quadratic mixtures (SNPALQ), extends the Successive
Nonnegative Projection Algorithm (SNPA), designed to address the unmixing
problem under a linear model. By explicitly modeling the product terms inherent
to the LQ model along the iterations of the SNPA scheme, the nonlinear
contributions in the mixing are mitigated, thus improving the separation
quality. The approach is shown to be relevant in a realistic numerical
experiment.
