[@Altmann2012ip] are used to solve the GBM- and PPNM-based unmixing problems, respectively. Interested readers are invited to refer to these works for detailed information regarding the optimization schemes.

Data description {#sec:data}
================

The mixing models and corresponding unmixing algorithms detailed in the previous sections are compared using simulated and real hyperspectral images. It is worth noting that, for both kinds of datasets, actual pure component spectral signatures (i.e., endmember spectra) and quantitative spatial distributions of these components (i.e., abundances) are available as ground truth in each pixel of the considered images. These datasets[^3] are described in this section.

Simulated dataset
-----------------

Two types of synthetic hyperspectral image data were generated from a ray tracing experiment. First, synthetic but realistic fully calibrated virtual scenes, namely citrus orchards and a forest, have been designed using methods developed in [@Stuckens2009] and [@VanderZande2008], respectively, which will be explained in more detail in the following paragraphs. Then, corresponding hyperspectral images have been simulated using an extended version of the physically based ray tracer (PBRT) [@Pharr2004]. In PBRT, a scene is defined using submodels to describe the various components of the scene: illumination sources, sensor platform, material optical properties, integrator and geometry descriptions. For the different 