Spatial–Spectral Hypergraph Dynamic Gating MLP Network for Hyperspectral Image Classification

Abstract

The advancement of spaceborne hyperspectral remote sensing technology has led to the widespread use of hyperspectral imaging, due to its ability to detect subtle spectral differences. Most of the traditional machine-learning (ML) methods and popular deep-learning (DL) architectures for hyperspectral image (HSI) classification either fail to capture global features or demand high computational resources. While multilayer perceptron (MLP)-based models offer a computationally efficient alternative, they struggle to capture manifold structures and are susceptible to overfitting. To address these challenges, we propose a novel spatial–spectral hypergraph dynamic gating MLP (S2H-DGMLP) framework tailored for HSI classification. The spatial–spectral hypergraph enhances discriminative power by modeling high-order spatial and spectral correlations, jointly optimizing local spatial features and global spectral features to produce more separable feature representations in the embedding space. Within this framework, the channel and spatial projections are statically parameterized using MLP, while the dynamic gating MLP (DGMLP) block captures global contextual information. The dynamic gating mechanism within the DGMLP block automatically adjusts the segmentation ratio to balance spatial and spectral contributions, while incorporating complex nonlinear combinations to improve feature representation. Experimental results on the Pavia University and Houston datasets demonstrate that S2H-DGMLP significantly improves classification performance, confirming its effectiveness in HSI classification tasks.