S²HGC: An End-to-End Spectral–Spatial Hypergraph Convolutional Network for Unsupervised Hyperspectral Band Selection

Band selection (BS) is an effective way to reduce redundancy and enhance processing efficiency by obtaining a subset with highly discriminative bands. However, existing methods focus on the correlations between neighboring bands, overlooking the nonlinear dependencies present in global contexts. Moreover, these approaches typically treat each band as an independent entity, disregarding the spatial structure and potential spectral-spatial information embedded within band combinations. To address these limitations, we propose S2HGC, an end-to-end spectral-spatial hypergraph convolutional network designed for unsupervised hyperspectral BS. Our method eliminates error accumulation from stepwise processing and enables the holistic optimization of band subsets. Specifically, a controllable autoencoder (CAE) is designed to reflect the low-dimensional representation of full bands and partition them into multiple combinations. Furthermore, spatial topology is produced from these band combinations using superpixels, and a spectral-spatial information fusion (S2IF) module is designed to extract spatial features and integrate them with spectral features. Finally, an adaptive hypergraph convolution is employed to capture the nonlinear correlations between bands and enhance the information interaction across long-distance bands. The critical bands are then selected to reconstruct the original hyperspectral imagery (HSI), which is iteratively optimized to obtain the optimal band subset. Extensive experiments on three publicly available datasets demonstrate the superior performance of S2HGC compared to other state-of-the-art BS methods.