Hyperspectral Band Selection Based on Adaptive Neighborhood Grouping and Local Structure Correlation

Abstract

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.