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SSA-GAN: Singular Spectrum Analysis-Enhanced Generative Adversarial Network for Multispectral Pansharpening

Author

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  • Lanfa Liu

    (State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau 999078, China
    Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
    Zhuhai MUST Science and Technology Research Institute, Zhuhai 519099, China
    CNSA Macau Center for Space Exploration and Science, Macao 999078, China)

  • Jinian Zhang

    (Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Baitao Zhou

    (Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Peilun Lyu

    (School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Zhanchuan Cai

    (State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau 999078, China
    Zhuhai MUST Science and Technology Research Institute, Zhuhai 519099, China
    School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China)

Abstract

Pansharpening is essential for remote sensing applications requiring high spatial and spectral resolution. In this paper, we propose a novel Singular Spectrum Analysis-Enhanced Generative Adversarial Network (SSA-GAN) for multispectral pansharpening. We designed SSA modules within the generator, enabling more effective extraction and utilization of spectral features. Additionally, we introduce Pareto optimization to the nonreference loss function to improve the overall performance. We conducted comparative experiments on two representative datasets, QuickBird and Gaofen-2 (GF-2). On the GF-2 dataset, the Peak Signal-to-Noise Ratio (PSNR) reached 30.045 and Quality with No Reference (QNR) achieved 0.920, while on the QuickBird dataset, PSNR and QNR were 24.262 and 0.817, respectively. These results indicate that the proposed method can generate high-quality pansharpened images with enhanced spatial and spectral resolution.

Suggested Citation

  • Lanfa Liu & Jinian Zhang & Baitao Zhou & Peilun Lyu & Zhanchuan Cai, 2025. "SSA-GAN: Singular Spectrum Analysis-Enhanced Generative Adversarial Network for Multispectral Pansharpening," Mathematics, MDPI, vol. 13(5), pages 1-13, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:745-:d:1599227
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