Spectral State Fusion Tree Mamba for Hyperspectral Image Classification

Hyperspectral image (HSI) data possess complex spatial structures and high-dimensional spectral information. Mamba has been applied to address the limitations of general methods in HSI classification, including restricted receptive fields and high computational complexity. However, the scan mechanism of traditional Mamba unreasonably constructs spatial distance relationships between neighboring row pixels and fails to adaptively construct the optimal scanning path based on the spectral similarity of pixels. Additionally, the characteristic of traditional Mamba scanning each channel independently overlooks the feature extraction from high-dimensional spectral information. This work proposes a Spectral State Fusion Tree Mamba (SSFTM) architecture to resolve these limitations. The Tree Scan (TS) mechanism computes cosine distances among spatial neighboring pixels and spectral channels to construct adaptive minimum spanning trees in both the spatial and spectral domains, thereby establishing reasonable spatial-spectral relationships and enabling efficient joint feature extraction. The Spectral State Fusion (SSF) mechanism applies multi-layer one-dimensional dilated convolutions along the spectral dimension to the state space vectors, enabling inter-channel interaction and promoting multi-scale spectral feature extraction. The proposed SSFTM demonstrates superior classification accuracy across multiple datasets compared to SOTA methods and exhibits acceptable computational complexity. The code is available at https://github.com/copawloroous/SSFTM.