MambaHSI+: Multidirectional State Propagation for Efficient Hyperspectral Image Classification

Hyperspectral image (HSI) classification faces significant challenges due to high-dimensional spectral redundancy and complex spatial-spectral dependencies. While existing MambaHSI models leverage state-space modeling to enhance representation learning, their unidirectional formulation fails to fully capture bidirectional spatial interactions and cross-band contextual dependencies. Moreover, the uniform projection mechanism struggles to effectively distinguish spectral variations across different wavelengths. To address these limitations, we propose MambaHSI+, a novel framework that integrates bidirectional state-space modeling with spectral trajectory learning (STL). The proposed architecture introduces three key innovations: 1) a bidirectional context modeling module, enhanced by reverse-order modeling, enables multidirectional spatial information propagation through recursive state transitions, facilitating comprehensive local-global feature aggregation while maintaining linear computational efficiency; 2) an STL paradigm that formulates spectral evolution as continuous state-space process with bidirectional propagation, effectively encoding cross-band relationships; and 3) a Mamba-enhanced channel attention mechanism that adaptively emphasizes discriminative spectral features via selective state-space transformations. Extensive experiments on four benchmark datasets demonstrate state-of-the-art (SOTA) performance, achieving an average accuracy (AA) improvement of 2.51% over MambaHSI. By integrating state-space systems with advanced Mamba mechanisms, MambaHSI+ establishes a new paradigm for spectral-spatial representation learning, significantly advancing classification accuracy while ensuring computational efficiency. The latest logs are now available at https://github.com/RockAilab/MambaHSI_Plus