Wavelet-Driven Spectral–Spatial State Space Modeling for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is challenged by high spectral dimensionality and complex spectral–spatial dependencies. Existing models, including CNNs, Transformers and Mamba, struggle to balance long-range modeling, computational efficiency, and preservation of spectral–spatial coherence. To address these limitations, we propose the Wavelet-Driven Spectral–Spatial State Space Network (WD4SNet), a novel and efficient architecture for HSI classification. Specifically, WD4SNet first employs a Spectral–Spatial Fusion Block (SSFB) to perform complementary integration of local spatial textures and spectral information at shallow layers, providing robust initial representations. The core of the network is a Spectral–Spatial State Space Model, which consists of a Spectral-Based Multi-Scale State Space (SMSS) and a Spatial-Axis Multi-Scale State Space (SASS). Both modules incorporate wavelet-guided enhancement with learnable subband processing, enabling multi-scale global and local spectral–spatial dependency modeling with linear complexity. To further enhance spectral–spatial synergy, a Cross-Axis Spectral–Spatial Feature Alignment (CASFA) module is introduced for adaptive alignment and fusion of spectral and spatial representations. Finally, a Lightweight Feature Pyramid Decoder (LFPD) reconstructs high-resolution prediction maps with minimal computational overhead. Extensive experiments on four benchmark hyperspectral datasets demonstrate that WD4SNet consistently outperforms state-of-the-art methods across multiple quantitative evaluation metrics. Furthermore, ablation studies validate the effectiveness of each module in advancing spectral–spatial fusion and representation learning.