Improved quantum evolutionary particle swarm optimization for band selection of hyperspectral image

Abstract

ABSTRACT 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.