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

Lanfa Liu, Jinian Zhang, Baitao Zhou, Peilun Lyu and Zhanchuan Cai ()
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Lanfa Liu: State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau 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

Mathematics, 2025, vol. 13, issue 5, 1-13

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.

Keywords: pansharpening; multispectral; GANs; Singular Spectrum Analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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