Hyperspectral Band Selection With Dynamic Graph Enhancement and Hierarchical Feature Fusion (opens in new tab)

Graph convolutional networks (GCNs) have been widely used in hyperspectral band selection (BS) and exhibit great potential. However, the substantial redundant information inherent in hyperspectral imagery and the fixed graph structure hinder the ability of GCNs to effectively capture the complex interband relationships. Moreover, most existing methods rely on a single criterion during the BS phase, often leading to biased results. To address these issues, this article proposes a dynamic graph...