Multi-Scale Wavelet and Graph Network with Spectral Self-Attention for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has gained increasing attention in remote sensing due to its fine-grained spectral information. However, existing methods still face significant challenges in preserving high-frequency details, modeling long-range dependencies, and integrating spectral, spatial, and frequency-domain features. In this work, we propose MWGN-SSA, a powerful network designed to enhance HSI classification by fusing multi-domain features. MWGN-SSA consists of three core modules: a Multi-Scale Learnable Wavelet Network (MLWN), a Window-Based Spectral Self-Attention (WSSA) mechanism, and a Deep-Hop Graph Convolutional Network (DH-GCN). First, MLWN adaptively decomposes HSIs into frequency subbands, retaining critical high-frequency textures for small or spectrally subtle targets. Second, WSSA captures both local and global spectral correlations using a windowed self-attention scheme. Third, DH-GCN constructs a deep graph structure to model spatial topology and overcome over-smoothing. A feature integration module (FIM) combines outputs from all branches for final prediction. Extensive experiments on four benchmark datasets demonstrate that MWGN-SSA achieves superior accuracy and robustness, particularly in complex and imbalanced HSI scenes.