Band selection is a direct and effective dimension reduction method and is one of the hotspots in hyperspectral remote sensing research. However, most of the methods ignore the orderliness and correlation of the selected bands and construct band subsets only according to the number of clustering centers desired by band sequencing. To address this issue, this article proposes a band selection method based on adaptive neighborhood grouping and local structure correlation (ANG-LSC). An adaptive subspace method is adopted to segment hyperspectral image cubes in space to avoid obtaining highly correlated subsets. Then, the product of local density and distance factor is utilized to sort each band and select the desired cluster center number. Finally, through the information entropy and correlation analysis of bands in different clusters, the most representative bands are selected from each cluster. Regarding evaluating the effectiveness of the proposed method, comparative experiments with the state-of-the-art methods are conducted on three public hyperspectral datasets. Experimental results demonstrate the superiority and robustness of ANG-LSC.

中文翻译：

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基于自适应邻域分组和局部结构相关性的高光谱波段选择

波段选择是一种直接有效的降维方法，是高光谱遥感研究的热点之一。但是，大多数方法忽略了所选波段的有序性和相关性，仅根据波段排序所需的聚类中心的数量来构建波段子集。为了解决这个问题，本文提出了一种基于自适应邻域分组和局部结构相关（ANG-LSC）的频带选择方法。采用自适应子空间方法对空间中的高光谱图像立方体进行分割，以避免获得高度相关的子集。然后，利用局部密度和距离因子的乘积对每个频带进行排序，并选择所需的群集中心编号。最后，通过信息熵和不同聚类中频带的相关性分析，从每个群集中选择最具代表性的频段。关于评估该方法的有效性，我们在三个公共高光谱数据集上进行了使用最新方法的对比实验。实验结果证明了ANG-LSC的优越性和鲁棒性。