Pan-Mamba: Effective pan-sharpening with State Space Model
<|reference_start|>Pan-Mamba: Effective pan-sharpening with State Space Model: 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}.<|reference_end|>
@article{he2024pan-mamba:,
    title={Pan-Mamba: Effective pan-sharpening with State Space Model},
    author={Xuanhua He, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man
  Zhou},
    journal={arXiv preprint arXiv:2402.12192},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12192},
    primaryClass={cs.CV}
}