Pan-sharpening involves integrating information from lowresolution 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 pansharpening 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 multispectral 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-theart 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 .

翻译：全色锐化旨在融合低分辨率多光谱图像与高分辨率全色图像，以生成高分辨率多光谱图像。尽管最近状态空间模型的进展（特别是Mamba模型在高效长距离依赖建模方面的突破）已彻底改变了计算机视觉领域，但其在全色锐化中的潜力尚未被挖掘，这促使我们开展探索。本文提出的Pan-Mamba是一种新型全色锐化网络，利用Mamba模型在全局信息建模中的高效性。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，专为高效跨模态信息交换与融合而设计。前者通过交换部分全色与多光谱通道实现轻量级跨模态交互，后者则通过利用内在的跨模态关系增强信息表示能力。通过涵盖多种数据集的广泛实验，我们的方法超越了现有最先进技术，展示了更优的全色锐化融合效果。据我们所知，本研究首次探索了Mamba模型在全色锐化中的潜力，并为该领域开辟了新方向。源代码已开源：https://github.com/alexhe101/Pan-Mamba。