approaches have been advocated to solve the inversion step, that can be formulated as a constrained optimization problem solved by fully constrained least square algorithms [@Heinz2001; @Theys2009; @Heylen2011tgrs] or as a statistical estimation problem solved within a Bayesian framework [@Dobigeon2008; @Eches2010ip; @Eches2011icassp].

However, for specific applications, LMM has demonstrated some difficulties to accurately describe real mixtures [@Dobigeon2014]. Notably, intimate mixtures of minerals are characterized by spatial scales typically smaller than the path length followed by the photons, which violates one fundamental assumption for considering a linear model. Analyzing such mixtures, e.g., composed of minerals, requires to resort to complex physical models coming from the radiative transfer theory. Various approximating models have been proposed in the spectroscopic literature, such as the popular Hapke’s model [@Hapke1981]. More recently, this model or related alternatives have been exploited in the hyperspectral literature to derive unmixing algorithms dedicated to remotely sensed images [@Guilfoyle2001; @Nascimento2010]. Broadwater *et al.* derived various kernel-based unmixing techniques that implicitly relied on the Hapke model [@Broadwater2009whispers; @Broadwater2010whispers; @Broadwater2011whispers]. In [@Close2012spie; @Close2012spieb; @Heylen2014], the authors combined linear and intimate mixing processes in single models to improve flexibility.

Conversely, scenes acquired over vegetated areas are also known to be subjected to more complex 