Abstract Swarm intelligence algorithms have been widely used in the dimensional reduction of hyperspectral remote sensing imagery. The ant colony algorithm (ACA), the clone selection algorithm (CSA), particle swarm optimization (PSO), and the genetic algorithm (GA) are the most representative swarm intelligence algorithms and have often been used as subset generation procedures in the selection of optimal band subsets. However, studies on their comparative performance for band selection have been rare. For this paper, we employed ACA, CSA, PSO, GA, and a typical greedy algorithm (namely, sequential floating forward selection (SFFS)) as subset generation procedures and used the average Jeffreys–Matusita distance (JM) as the objective function. In this way, the band selection algorithm based on ACA (BS-ACA), band selection algorithm based on CSA (BS-CSA), band selection algorithm based on PSO (BS-PSO), band selection algorithm based on GA (BS-GA), and band selection algorithm based on SFFS (BS-SFFS) were tested and evaluated using two public datasets (the Indian Pines and Pavia University datasets). To evaluate the algorithms’ performance, the overall classification accuracy of maximum likelihood classifier and the average runtimes were calculated for band subsets of different sizes and were compared. The results show that the band subset selected by BS-PSO provides higher overall classification accuracy than the others and that its runtime is approximately equal to BS-GA’s, higher than those of BS-ACA, BS-CSA, and BS-SFFS. However, the premature characteristic of BS-ACA makes it unacceptable, and its average JM is lower than those of other algorithms. Furthermore, BS-PSO converged in 500 generations, whereas the other three swarm-intelligence based algorithms either ran into local optima or took more than 500 generations to converge. BS-PSO was thus proved to be an excellent band selection method for a hyperspectral image.

中文翻译：

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高光谱遥感影像优化波段选择的群智能算法比较

摘要 群智能算法已广泛应用于高光谱遥感影像的降维。蚁群算法（ACA）、克隆选择算法（CSA）、粒子群优化（PSO）和遗传算法（GA）是最具代表性的群体智能算法，经常被用作选择群体的子集生成程序。最优波​​段子集。然而，关于它们在频段选择方面的比较性能的研究很少。对于本文，我们采用 ACA、CSA、PSO、GA 和典型的贪婪算法（即顺序浮动前向选择（SFFS））作为子集生成程序，并使用平均 Jeffreys-Matusita 距离（JM）作为目标函数。这样，基于ACA（BS-ACA）的频段选择算法，测试了基于CSA的频段选择算法（BS-CSA）、基于PSO的频段选择算法（BS-PSO）、基于GA的频段选择算法（BS-GA）和基于SFFS的频段选择算法（BS-SFFS）并使用两个公共数据集（印度松树和帕维亚大学数据集）进行评估。为了评估算法的性能，对不同大小的波段子集计算了最大似然分类器的总体分类精度和平均运行时间，并进行了比较。结果表明，BS-PSO 选择的波段子集比其他波段子集提供更高的整体分类精度，其运行时间约等于 BS-GA，高于 BS-ACA、BS-CSA 和 BS-SFFS。然而，BS-ACA的过早特性使其无法接受，并且其平均 JM 低于其他算法。此外，BS-PSO 在 500 代内收敛，而其他三种基于群智能的算法要么遇到局部最优，要么需要 500 多代才能收敛。因此，BS-PSO 被证明是一种极好的高光谱图像波段选择方法。