A band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the bands with low correlation and a large amount of information into the target set to reach dimensionality reduction for HSI via two phases. Specifically, the fast density peaks clustering (FDPC) algorithm is used to select the most representative node in each cluster to build a candidate set at first. During the implementation, we normalize the local density and relative distance and utilize the dynamic cutoff distance to weaken the influence of density so that the selection is more likely to be carried out in scattered clusters than in high-density ones. After that, we conduct a further selection in the candidate set using mRMR strategy and comprehensive measurement of information (CMI), and the eventual winners will be selected into the target set. Compared with other six state-of-the-art unsupervised algorithms on three real-world HSI data sets, the results show that TLS can group the bands with lower correlation and richer information and has obvious advantages in indicators of overall accuracy (OA), average accuracy (AA), and Kappa coefficient.

中文翻译：

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一种基于两层选择策略的高光谱影像最优波段组合搜索方法

本文提出了一种基于两层选择 (TLS) 策略的波段选择方法，该方法从所有波段集形成最佳子集以重建原始高光谱图像 (HSI)，旨在以更少的波段获得更好的性能。顾名思义，TLS 将低相关性和大量信息的频段挑选出目标集，以通过两个阶段实现 HSI 的降维。具体来说，首先使用快速密度峰值聚类（FDPC）算法选择每个簇中最具代表性的节点来构建候选集。在实现过程中，我们将局部密度和相对距离归一化，并利用动态截止距离来减弱密度的影响，使选择更容易在分散的簇中进行，而不是在高密度的簇中进行。之后，我们使用 mRMR 策略和综合信息测量（CMI）在候选集中进行进一步的选择，最终获胜者将被选入目标集中。在三个真实世界的 HSI 数据集上与其他六种最先进的无监督算法相比，结果表明 TLS 可以对相关性较低、信息更丰富的频段进行分组，并且在整体准确度（OA）指标上具有明显优势，平均准确率 (AA) 和 Kappa 系数。