Pan-Mamba: Effective pan-sharpening with State Space Model

• URL: http://arxiv.org/abs/2402.12192v2
• Date: Sat, 9 Mar 2024 03:16:25 GMT
• Title: Pan-Mamba: Effective pan-sharpening with State Space Model
• Authors: Xuanhua He, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou
• Abstract summary: Pan-Mamba represents a novel pan-sharpening network that leverages the efficiency of the Mamba model in global information modeling. Our proposed approach surpasses state-of-the-art methods, showcasing superior fusion results in pan-sharpening. This work is the first attempt in exploring the potential of the Mamba model and establishes a new frontier in the pan-sharpening techniques.
• Score: 21.032910745931936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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