The large number of bands, the huge amount of information and the high band correlation in hyperspectral images bring great difficulties to the band selection of hyperspectral images. In the basic Particle Swarm Optimization (PSO), the learning factor and inertia factor are fixed, which limits the exploratory ability of the algorithm and cannot balance the global and local relations well. Therefore, a new algorithm-Improved Quantum Evolutionary Particle Swarm Optimization (IQEPSO) is proposed that the learning factor and inertia factor could be changed with the number of iterations. At the same time, mutation probability could be changed with the number of invariable fitness values, in order to enable particles to jump out of the local optimum. The proposed algorithm is applied to band selection of hyperspectral images. Experiments show that the proposed algorithm can improve the classification accuracy of ground objects. The disadvantage of falling into local optimum is overcome, the convergence speed is accelerated, and better classification accuracy is obtained.

高光谱图像中的波段数量多，信息量大，波段相关性高，给高光谱图像的波段选择带来很大困难。在基本粒子群优化算法（PSO）中，学习因子和惯性因子是固定的，这限制了算法的探索能力，无法很好地平衡全局和局部关系。因此，提出了一种新的算法-改进的量子进化粒子群算法（IQEPSO），该算法可以随着迭代次数的变化而改变学习因子和惯性因子。同时，突变概率可以随不变适应度值的数量而变化，以使粒子能够跳出局部最优值。将该算法应用于高光谱图像的波段选择。实验表明，该算法可以提高地面物体的分类精度。克服了陷入局部最优的缺点，加快了收敛速度，获得了更好的分类精度。