  Pan-sharpening involves integrating information from low-resolution
multi-spectral and high-resolution panchromatic images to generate
high-resolution multi-spectral counterparts. While recent advancements in the
state space model, particularly the efficient long-range dependency modeling
achieved by Mamba, have revolutionized computer vision community, its untapped
potential in pan-sharpening motivates our exploration. Our contribution,
Pan-Mamba, represents a novel pan-sharpening network that leverages the
efficiency of the Mamba model in global information modeling. In Pan-Mamba, we
customize two core components: channel swapping Mamba and cross-modal Mamba,
strategically designed for efficient cross-modal information exchange and
fusion. The former initiates a lightweight cross-modal interaction through the
exchange of partial panchromatic and multi-spectral channels, while the latter
facilities the information representation capability by exploiting inherent
cross-modal relationships. Through extensive experiments across diverse
datasets, our proposed approach surpasses state-of-the-art methods, showcasing
superior fusion results in pan-sharpening. To the best of our knowledge, this
work is the first attempt in exploring the potential of the Mamba model and
establishes a new frontier in the pan-sharpening techniques. The source code is
available at \url{https://github.com/alexhe101/Pan-Mamba}.
