Multiscale 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 finegrained 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 multidomain features. MWGN-SSA consists of three core modules: a multiscale 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 oversmoothing. A feature integration module 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.