A quantum-behaved particle swarm optimization for hyperspectral endmember extraction

In this paper, endmember extraction algorithm is described as a combinatorial optimization problem. A novel quantum-behaved particle swarm optimization (QPSO) approach which employs quantum-behaved particle swarm optimization to find endmembers with good performance is proposed. As far as our knowledge, it is the first time that quantum-behaved particle swarm optimization is introduced into hyperspectral endmember extraction. In order to follow the law of particle movement, a high dimensional particles definition is proposed. The proposed algorithm was tested and evaluated by both synthetic and real hyperspectral data sets. Experimental results indicate that the proposed method get a better result compared to the algorithms of vertex component analysis (VCA), N-FINDR and discrete particle swarm optimization (D-PSO).