A wide variety of swarm intelligence algorithm-based approaches have been recently developed for selecting the near-optimal bands from hyperspectral images (HSIs). However, such methods [including the nondominated sorting genetic algorithm (NSGA)] for HSIs pixel classification are limited by the lack of effective initialization and directional evolution. This research proposed a successive projections algorithm (SPA) and individual repair operation for NSGA (named Sr-NSGA) for band selection. Specifically, Sr-NSGA used the SPA to initialize the population and construct repair sequences that optimize new generated individuals in evolution. Meanwhile, with the guidance of two mutually restricted fitness functions, i.e., average mutual information and classification accuracy, Sr-NSGA searched for the near-optimal band set in an iterative way. Different combinations obtained by SR-NSGA and three effective band selection methods were tested and compared on the Botswana, KSC, and Indian Pines datasets. The results show that Sr-NSGA yielded better performance than the other three methods. Furthermore, support vector machine was used as the classifier for the pixel classification of the Indian Pines dataset to test Sr-NSGA. Experimental result show that Sr-NSGA achieves an overall accuracy of 95.57% and adapts to different classifiers.

中文翻译：

---

基于双评价测度和改进非支配排序遗传算法的高光谱波段选择

最近开发了多种基于群智能算法的方法，用于从高光谱图像 (HSI) 中选择接近最佳的波段。然而，这种用于 HSI 像素分类的方法 [包括非支配排序遗传算法 (NSGA)] 受到缺乏有效初始化和定向进化的限制。这项研究提出了一种连续投影算法（SPA）和 NSGA（命名为 Sr-NSGA）的单独修复操作，用于频带选择。具体来说，Sr-NSGA 使用 SPA 来初始化种群并构建修复序列，以优化进化中新生成的个体。同时，在平均互信息和分类准确率这两个相互限制的适应度函数的指导下，Sr-NSGA以迭代的方式寻找接近最优的波段集。通过 SR-NSGA 和三种有效波段选择方法获得的不同组合在博茨瓦纳、KSC 和印度松树数据集上进行了测试和比较。结果表明，Sr-NSGA 的性能优于其他三种方法。此外，支持向量机被用作印度松树数据集像素分类的分类器，以测试 Sr-NSGA。实验结果表明，Sr-NSGA 的总体准确率达到了 95.57%，并适应了不同的分类器。支持向量机被用作印度松树数据集像素分类的分类器，以测试 Sr-NSGA。实验结果表明，Sr-NSGA 的总体准确率达到了 95.57%，并适应了不同的分类器。支持向量机被用作印度松树数据集像素分类的分类器，以测试 Sr-NSGA。实验结果表明，Sr-NSGA 的总体准确率达到了 95.57%，并适应了不同的分类器。