Independent component analysis based on improved quantum genetic algorithm: Application in hyperspectral images

To avoid the restriction of neuron activation functions of neural learning algorithm and the disadvantage of getting into local optimum solution with general numerical computation method, a novel independent component analysis (ICA) based on improved quantum genetic algorithm (IQGA) is proposed in our paper. Moreover, Han's quantum genetic algorithm (QGA) is improved by adopting the quantum crossover and quantum mutation to overcome the premature convergence and increase the search capability in our work. The proposed algorithm is applied to hyperspectral anomaly detection. The effectiveness of the algorithm is evaluated by HYDICE hyperspectral images. It is demonstrated that the proposed algorithm has better detection effect and time efficiency than QGA based ICA for the hyperspectral anomaly detection task.