SSFNet: Spatial–Spectral Frequency-Aware Network for Hyperspectral Image Classification

Spatial–spectral modeling methods have achieved remarkable progress in hyperspectral image (HSI) classification. However, most existing approaches primarily emphasize spatial–spectral feature fusion or multi-scale representation learning, while the intrinsic frequency-discriminative characteristics of spatial and spectral signals remain underexplored for coordinated local–global modeling. To address these issues, we propose a novel Spatial–Spectral Frequency-Aware Network (SSFNet) for hyperspectral image classification. SSFNet adopts a dual-branch architecture, consisting of a Frequency-Aware Spatial Modeling (FASM) branch and a Frequency-Based Spectral Modeling (FBSM) branch. The spatial branch (FASM) enhances boundary sensitivity and global contextual consistency through explicit high–low frequency modulation, enabling improved spatial representation learning. The FBSM branch performs spectral frequency decomposition, where the low-frequency component captures smooth, large-scale spectral trends, and the high-frequency component models fine-grained variations and discriminative fluctuations. A gated fusion mechanism is further designed to adaptively integrate complementary frequency features across different scales. Extensive experiments on four benchmark datasets demonstrate that SSFNet consistently outperforms eight state-of-the-art methods in terms of classification accuracy and generalization capability. These results verify the effectiveness of explicit frequency-aware spatial–spectral modeling for hyperspectral image classification.