HyperEAST: An Enhanced Attention-Based Spectral-Spatial Transformer with Self-Supervised Pretraining for Hyperspectral Image Classification

Hyperspectral images (HSIs) are essential in geoscientific applications such as resource exploration, precision agriculture, and environmental monitoring due to their rich spectral-spatial information. However, existing classification methods face notable limitations: PCA ignores spatial context, CNNs lack long-range modeling, and Vision Transformer (ViT)-based models often overfit under label-scarce conditions due to their high capacity and modality-agnostic design. To address these challenges, we propose HyperEAST, an efficient dual-branch ViT framework that explicitly decouples spectral and spatial feature modeling. At its core is a novel Linear Fusion Attention Mechanism (LFAM), which replaces dot-product attention with a softmax-free additive formulation based on lightweight convolutions, enabling local-global representation learning with linear complexity. To enhance robustness under limited labels, we adopt a modality-aware masked image modeling (MIM) strategy that separately reconstructs masked spectral and spatial tokens during self-supervised pretraining. We further introduce a dataset-aware hybrid loss combining cross-entropy and focal loss to mitigate class imbalance and sharpen decision boundaries. Experiments on four benchmark HSI datasets-WHU-Hi-HC, WHU-Hi-LK, Indian Pines, and Pavia University-demonstrate that HyperEAST achieves competitive accuracy, efficiency, and robustness. Code is available at https://github.com/JaylenTang/HyperEAST.