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Quantum machine learning for fusion of multichannel optical satellite images

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Category: Content №5 2025

Last Updated on 25 October 2025

Published on 30 November -0001

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Authors:

V. Yu. Kashtan, orcid.org/0000-0002-0395-5895, Dnipro University of Technology, Dnipro, Ukraine

V. V. Hnatushenko*, orcid.org/0000-0003-3140-3788, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

K. Wereszczyński, orcid.org/0000-0003-1686-472X, Silesian University of Technology, Gliwice, Republic of Poland

K. Cyran, orcid.org/0000-0003-1789-4939, Silesian University of Technology, Gliwice, Republic of Poland

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (5): 112 - 121

https://doi.org/10.33271/nvngu/2025-5/112

Abstract:

Purpose. To develop a novel approach for fusion of optical satellite images based on machine learning and quantum optimization for integrating spatial-spectral information from RGB and IR channels.

Methodology. The proposed approach involves sequential processing of input data, including geometric, radiometric, and atmospheric corrections. Each channel is decomposed into low-frequency and high-frequency components using a Gaussian filter. The Independent Component Analysis (ICA) method is applied to reduce the dimensionality of input data. A quantum optimizer approximation algorithm is applied to analyze the infrared channel. A deep convolutional neural network with residual dense blocks is used to extract spatial structural features from RGB channels. After integrating features through fully connected layers, the quantum block optimizes the weight coefficients for the final channel fusion.

Findings. Quantitative evaluation demonstrates that the proposed approach outperforms classical fusion methods, including Brovey, Gram-Schmidt, IHS, HCS, HPFC, ATWT, PCA, and CNN, in spectral and spatial information integration accuracy. The method achieves the lowest mean squared error (MSE = 191.8), high structural similarity index (SSIM = 0.43), entropy (Entropy = 7.54), and Sobel filter range (Sobel Sharp = 19.19–21.67 across R, G, B channels). Visual analysis also confirms qualitative advantages: images exhibit clear structure without artifacts and balanced color reproduction consistent with the spectral characteristics of the original RGB data.

Originality. A novel approach to utilizing information of the IR channel is proposed, which integrates a quantum-classical algorithm within a deep convolutional neural network architecture for synergistic processing of multichannel optical images using multilevel frequency decomposition and a weighted feature fusion mechanism.

Practical value. The proposed approach can be implemented in Earth remote sensing systems to enhance the quality of satellite image processing, particularly for mapping, land resource monitoring, agricultural control, and environmental analysis tasks. Applying quantum algorithms opens new opportunities for improving efficiency and accuracy in processing multidimensional geoinformation data containing IR channel information.

Keywords: quantum machine learning, data fusion, neural network, satellite imagery

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