The linear mixed model constrained particle swarm optimization for hyperspectral endmember extraction from highly mixed data

Spectral unmixing is one of the most important techniques for analyzing hyperspectral images and many hyperspectral unmixing algorithms were developed under an assumption that pure pixels exist in recent years. However, the pure-pixel assumption may be seriously violated for highly mixed data. Endmember extraction can be regards as an optimization problem no matter whether pure-pixel exists or not. In this paper, we incorporate linear mixed model and particle swarm optimization to develop a linear mixed model constrained particle swarm optimization (LMMC-PSO) for endmember extraction from highly mixed data. Each particle in LMMC-PSO moves in search space according to linear mixed model rather than with a velocity, which is dynamically adjusted according to its own optimal position and global optimum of all particles. The experimental results indicated that the proposed method obtained better results than the algorithms of VCA, MVC-NMF, MVSA, MVES, and SISAL.