Improvements to algorithms for hyperspectral linear unmixing based on statistical model

Spectral mixing is one of the main problems that arise when characterizing the spectral constituents residing at a sub-pixel level in a hyperspectral scene. In this work we propose a improvement of the algorithms based on statistical model, i.e. NCM, with a novel sampling approach inspired by Genetic Algorithms. Furthermore, linearization is introduced to reduce computational complexity.