S2PW-Mamba: Pinwheel and Wavelet-based Spatial-Spectral Mamba for Hyperspectral Image Classification

Recently, the selective structured state space model (S6) built upon the Mamba architecture has attracted widespread attention for its outstanding performance in long-range modeling. However, existing Mamba-based hyperspectral image (HSI) classification methods suffer from certain limitations in extracting edge information. To address this problem, a new HSI classification framework called Pinwheel and Wavelet-based Spatial-Spectral Mamba (S 2 PW-Mamba) is proposed in this paper. The input to our S 2 PW-Mamba is the complete, unsegmented image, which preserves the correlation between pixels in the HSI while leveraging convolution to enhance the extraction of local information. Specifically, two S6 modules are employed along different scanning directions to extract the spatial-spectral features from the HSI, acting on the spectral and spatial domains, respectively. Meanwhile, a GateFusion module is designed to adaptively guide the fusion of spatial and spectral features by discerning their relative importance. More precisely, our S 2 PW-Mamba first extracts both edge and central spatial features using a Pinwheel-shaped convolution and a four-directional spatial scanning module, effectively capturing spatial-contextual relationships. Subsequently, by integrating the wavelet transform with the S6 architecture, the model is capable of capturing both global contextual dependencies and local texture details within the spectral domain. Comprehensive experiments on three benchmark HSI datasets show that our approach outperforms existing state-of-the-art methods.