L0-Norm based Image Pansharpening by using population-based algorithms

Mehmet Akif Günen; María-Luisa Pérez-Delgado; Erkan Beşdok; Mehmet Akif Günen; María-Luisa Pérez-Delgado; Erkan Beşdok

doi:10.3934/math.20241561

AIMS Mathematics

2024, Volume 9, Issue 11: 32578-32628. doi: 10.3934/math.20241561

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L0-Norm based Image Pansharpening by using population-based algorithms

1.
Department of Geomatics Engineering, Faculty of Engineering and Natural Sciences, Gümüşhane University, 29100, Gümüşhane, Türkiye
2.
Department of Computer Science and Automatics, Universidad de Salamanca, Escuela Politécnica Superior de Zamora, Av. Requejo, 33, Zamora, 49022, Spain
3.
Engineering Faculty, Department of Geomatics Engineering, Erciyes University, Kayseri, Türkiye

Received: 05 September 2024 Revised: 30 October 2024 Accepted: 04 November 2024 Published: 18 November 2024
MSC : 68410, 68T05, 68T20, 68W50, 94a08

Earth observation satellites capture panchromatic images at high spatial resolution and multispectral images at lower resolution to optimize the use of their onboard energy sources. This results in a technical necessity to synthesize high-resolution multispectral images from these data. Pansharpening techniques aim to combine the spatial detail of panchromatic images with the spectral information of multispectral images. However, due to the discrete nature of these images and their varying local statistical properties, many pansharpening methods suffer from numerical artifacts such as chromatic and spatial distortions. This paper introduces the L0-Norm-based pansharpening method (L0pan), which addressed these challenges by maximizing the number of similar pixels between the synthesized pansharpened image and the original panchromatic and multispectral images. L0pan was optimized using a population-based colony search algorithm, enabling it to effectively balance both chromatic fidelity and spatial resolution. Extensive experiments across nine different datasets and comparison with nine other pansharpening methods using ten quality metrics demonstrated that L0pan significantly outperformed its counterparts. Notably, the colony search algorithm yielded the best overall results, highlighting the algorithm's strength in refining pansharpening accuracy. This study contributed to the advancement of pansharpening techniques, offering a method that preserved both chromatic and spatial details more effectively than existing approaches.
- Pansharpening,
- image fusion,
- Population-based algorithms,
- Colony Search Algorithm
Citation: Mehmet Akif Günen, María-Luisa Pérez-Delgado, Erkan Beşdok. L0-Norm based Image Pansharpening by using population-based algorithms[J]. AIMS Mathematics, 2024, 9(11): 32578-32628. doi: 10.3934/math.20241561

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Abstract

Earth observation satellites capture panchromatic images at high spatial resolution and multispectral images at lower resolution to optimize the use of their onboard energy sources. This results in a technical necessity to synthesize high-resolution multispectral images from these data. Pansharpening techniques aim to combine the spatial detail of panchromatic images with the spectral information of multispectral images. However, due to the discrete nature of these images and their varying local statistical properties, many pansharpening methods suffer from numerical artifacts such as chromatic and spatial distortions. This paper introduces the L0-Norm-based pansharpening method (L0pan), which addressed these challenges by maximizing the number of similar pixels between the synthesized pansharpened image and the original panchromatic and multispectral images. L0pan was optimized using a population-based colony search algorithm, enabling it to effectively balance both chromatic fidelity and spatial resolution. Extensive experiments across nine different datasets and comparison with nine other pansharpening methods using ten quality metrics demonstrated that L0pan significantly outperformed its counterparts. Notably, the colony search algorithm yielded the best overall results, highlighting the algorithm's strength in refining pansharpening accuracy. This study contributed to the advancement of pansharpening techniques, offering a method that preserved both chromatic and spatial details more effectively than existing approaches.

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