Interpretable Spectral-State Learning via Unmixing-Guided Mamba Attention Networks for Hyperspectral Image Classification
A spectral-guided state-space attention network (SGSA-Net) for hyperspectral image classification, which incorporates bidirectional Mamba-based spectral sequence modeling, unmixing-aware representation learning, spatial attention, and multi-level explainability is proposed.