S²CMamba: A Mamba-Based Pansharpening Model Incorporating Spatial and Spectral Consistency

Prevailing pansharpening methods tackle the ill-posed challenge of reconstructing high-spatial-resolution multispectral (HRMS) images from low-spatial-resolution multispectral (LRMS) and panchromatic (PAN) inputs. This ill-posed nature introduces distortions such as blurred spatial edges and spectral color deviations, which compromise the fidelity of the reconstructed image. To address this, we propose S2CMamba, a dual-branch framework that leverages Mamba’s efficient contextual modeling and enforces spatial and spectral consistency through tailored priors, effectively alleviating the ill-posed nature of the pansharpening. The spatial context branch utilizes the windowed spatial local Mamba (WSLM) for local details and the global spatial interaction Mamba (GSIM) for long-range structures. In WSLM, a manifold preservation (MP) constraint is proposed to align HRMS features with the low-dimensional manifold consistency of PAN and LRMS, thereby mitigating high-dimensional distortions and enhancing spatial consistency. Meanwhile, the spectral branch integrates multiscale feature extraction and designs the spectral context Mamba (SCM) to capture spectral context. Moreover, the spatial and spectral properties of multispectral (MS) images are investigated, and the wavelet transforms are introduced to better accomplish consistency. By incorporating contextual information, S2CMamba reduces the ambiguity of the solution space, while wavelet-based consistency constraints and a designed MP prior further alleviate the ill-posed nature. Extensive experiments on benchmark datasets demonstrate that S2CMamba surpasses state-of-the-art (SOTA) methods, validating the efficacy of this approach in addressing the ill-posed nature of pansharpening tasks.