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 https://github.com/alexhe101/Pan-Mamba.

中文翻译：

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Pan-Mamba：使用状态空间模型进行有效的全色锐化


全色锐化涉及整合来自低分辨率多光谱和高分辨率全色图像的信息，以生成高分辨率多光谱对应图像。虽然状态空间模型的最新进展，特别是 Mamba 实现的高效远程依赖关系建模，已经彻底改变了计算机视觉社区，但它在全色锐化方面尚未开发的潜力激发了我们的探索。我们的贡献 Pan-Mamba 代表了一种新的全色锐化网络，它利用了 Mamba 模型在全球信息建模中的效率。在 Pan-Mamba 中，我们定制了两个核心组件：通道交换 Mamba 和跨模态 Mamba，策略性地设计为高效的跨模态信息交换和融合。前者通过交换部分全色和多光谱通道来启动轻量级跨模态交互，而后者则通过利用固有的跨模态关系来提供信息表示能力。通过对不同数据集的广泛实验，我们提出的方法超越了最先进的方法，展示了全色锐化的卓越融合结果。据我们所知，这项工作是探索 Mamba 模型潜力的第一次尝试，并在全色锐化技术中建立了一个新的领域。源代码可在 https://github.com/alexhe101/Pan-Mamba 获取。