GABA算法的遥感图像分类

为了弥补蝙蝠算法后期收敛速度慢、寻优精度不高、易陷入局部最优值的缺点，本文提出了一种新的遥感图像分类算法--GABA算法，该算法将遗传算法中的选择、交叉、变异操作应用到蝙蝠算法中，使蝙蝠算法具有变异机制，避免种群个体陷入局部最优，提高了算法全局寻优能力，增加了蝙蝠算法的多样性。同时，为了突出本文算法的优点，试验将蝙蝠算法、K-means算法、粒子群算法与本文算法结果进行比较，分析评价遥感图像的分类结果。试验表明本文算法在遥感图像分类应用中既提高了分类精度又减少了分类时间，是一种可行、有效的遥感图像分类方法。

Remote Sensing Image Classification of GABA Algorithm

In order to make up for the shortcomings of bat algorithm optimization in late, which convergence rate is slow, the accuracy is not high, and easy to fall into the local optimum, this paper proposes a new classification algorithm-GABA algorithm for remote sensing image, and in the algorithm, the operations of genetic algorithm, including selection, crossover, mutation operator are applied to the bat algorithm.It turns out that the bat algorithm with mutation mechanism to avoid the population into local optimum, improve the ability of global optimization algorithm to increase the diversity of bat algorithm.As the same time, in order to highlight the advantages of this algorithm, experiments compared the results of bat algorithm, K-Means algorithm, particle swarm algorithm and the algorithm, analyze and evaluated the classification results of remote sensing images.And experiments show that in the application of remote sensing image classification, this algorithm can not only improve the classification accuracy, but also reduce the classification time.It is a feasible and effective method for remote sensing image classification.