Conditional Skipping Mamba Network for Pan-Sharpening

Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by combining high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) data, while maintaining the symmetry of spatial and spectral characteristics. Traditional convolutional neural networks (CNNs) struggle with global dependency modeling due to local receptive fields, and Transformer-based models are computationally expensive. Recent Mamba models offer linear complexity and effective global modeling. However, existing Mamba-based methods lack sensitivity to local feature variations, leading to suboptimal fine-detail preservation. To address this, we propose a Conditional Skipping Mamba Network (CSMN), which enhances global-local feature fusion symmetrically through two modules: (1) the Adaptive Mamba Module (AMM), which improves global perception using adaptive spatial-frequency integration; and (2) the Cross-domain Mamba Module (CDMM), optimizing cross-domain spectral-spatial representation. Experimental results on the IKONOS and WorldView-2 datasets demonstrate that CSMN surpasses existing state-of-the-art methods in achieving superior spectral consistency and preserving spatial details, with performance that is more symmetric in fine-detail preservation.