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Keywords = Fourier implicit fusion neural network

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23 pages, 4335 KB  
Article
Fourier Fusion Implicit Mamba Network for Remote Sensing Pansharpening
by Ze-Zheng He, Hong-Xia Dou and Yu-Jie Liang
Remote Sens. 2025, 17(22), 3747; https://doi.org/10.3390/rs17223747 - 18 Nov 2025
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Abstract
Pansharpening seeks to reconstruct a high-resolution multi-spectral image (HR-MSI) by integrating the fine spatial details from the panchromatic (PAN) image with the spectral richness of the low-resolution multi-spectral image (LR-MSI). In recent years, Implicit Neural Representations (INRs) have demonstrated remarkable potential in various [...] Read more.
Pansharpening seeks to reconstruct a high-resolution multi-spectral image (HR-MSI) by integrating the fine spatial details from the panchromatic (PAN) image with the spectral richness of the low-resolution multi-spectral image (LR-MSI). In recent years, Implicit Neural Representations (INRs) have demonstrated remarkable potential in various visual domains, offering a novel paradigm for pansharpening tasks. However, traditional INRs often suffer from insufficient global awareness and a tendency to capture mainly low-frequency information. To address these challenges, we present the Fourier Fusion Implicit Mamba Network (FFIMamba). The network takes advantage of Mamba’s ability to capture long-range dependencies and integrates a Fourier-based spatial–frequency fusion approach. By mapping features into the Fourier domain, FFIMamba identifies and emphasizes high-frequency details across spatial and frequency dimensions. This process broadens the network’s perception area, enabling more accurate reconstruction of fine structures and textures. Moreover, a spatial–frequency interactive fusion module is introduced to strengthen the information exchange among INR features. Extensive experiments on multiple benchmark datasets demonstrate that FFIMamba achieves superior performance in both visual quality and quantitative metrics. Ablation studies further verify the effectiveness of each component within the proposed framework. Full article
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