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Journals / International Journal of Environment and Geoinformatics / 2026 / Volume: 8 Issue: 2

Comparative Analysis on Deep Learning based Pan-sharpening of Very High-Resolution Satellite Images

Peijuan Wang(ISTANBUL TECHNICAL UNIVERSITY)0000-0003-2981-7711
Ugur Alganci(Istanbul Technical University)0000-0002-5693-3614
Elif Sertel(ISTANBUL TECHNICAL UNIVERSITY)0000-0003-4854-494X
Pages
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Publication Date
2021-06-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to results, the deep learning-based methods provided promising results in both the reduced resolution and full resolution experiments, while PRACS method outperformed other traditional algorithms in most of the experimental configurations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Pan-sharpening is a fundamental task of remote sensing, aiming to produce a synthetic image having high spatial and spectral resolution of original panchromatic and multispectral images.
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Key Findings & Data:In this research, a detailed comparative analysis was conducted to evaluate the performance and visual quality of pan-sharpening results from traditional algorithms and deep learning-based models.
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Conclusion & Impact:According to results, the deep learning-based methods provided promising results in both the reduced resolution and full resolution experiments, while PRACS method outperformed other traditional algorithms in most of the experimental configurations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Pan-sharpening · Deep Learning · Convolutional Neural Network

Abstract

Pan-sharpening is a fundamental task of remote sensing, aiming to produce a synthetic image having high spatial and spectral resolution of original panchromatic and multispectral images. In recent years, as in other tasks of the remote sensing field, deep learning based approaches have been developed for this task. In this research, a detailed comparative analysis was conducted to evaluate the performance and visual quality of pan-sharpening results from traditional algorithms and deep learning-based models. For this purpose, the deep learning based methods that are CNN based pan-sharpening (PNN), Multiscale and multi-depth convolutional neural networks (MSDCNN) and Pan-sharpened Generative Adversarial Networks (PSGAN) and traditional methods that are Brovey, PCA, HIS, Indusion and PRACS were applied. Analysis were performed on regions with different land cover characteristics to evaluate the stability of the methods. In addition, effects of the filter size, spectral indices, activation and loss functions on the pan-sharpening were investigated. For the accuracy assessment, commonly used with-reference and without-reference quality metrics were computed in addition to visual quality evaluations. According to results, the deep learning-based methods provided promising results in both the reduced resolution and full resolution experiments, while PRACS method outperformed other traditional algorithms in most of the experimental configurations.

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Cite This Article

Wang, P., Alganci, U., Sertel, E. (2021). Comparative Analysis on Deep Learning based Pan-sharpening of Very High-Resolution Satellite Images. International Journal of Environment and Geoinformatics, Volume: 8 Issue: 2. https://doi.org/10.30897/ijegeo.834760

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