Hyperspectral images provide the capability of identifying materials at a sub-pixel level by unmixing the spectra measured at each pixel. Many unmixing algorithms use a model of the mixing process. Unmixing is then approached as an inverse problem: find the spectra that were mixed according to the model to produce a given measurement, or set of measurements. Over the past decade, the prevalent mixing model investigated is the linear mixing model. Many unmixing techniques based on this model have been proposed.