Mamba-based multiview spatial and frequency collaborative network for hyperspectral image unmixing

Hyperspectral unmixing is a critical task in remote sensing, challenged by complex mixing mechanisms and long-range spatial dependency modeling. To address the limitations of existing methods in capturing spatial structures and frequency representations, we propose SFMamba, a novel spatial-frequency collaborative unmixing framework. The framework includes two key modules: (1) a multi-view spatial mamba module based on state space model, which captures directional spatial dependencies to enhance contextual representation; and (2) a hyperspectral fourier mixer, which integrates fourier transform-based frequency features to strengthen the spectral modeling of endmembers. Experiments on the Urban and Jasper Ridge datasets demonstrate that the proposed method achieves superior performance in endmember extraction, with competitive results in abundance estimation, thus showing strong potential in hyperspectral unmixing tasks. This work provides a novel and effective solution for hyperspectral unmixing with spatial-frequency collaboration.