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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

References.

1. Meher, B., Agrawal, S., Panda, R., & Abraham, A. (2019). A survey on region based image fusion methods. Information Fusion, 48, 119-132. https://doi.org/10.1016/j.inffus.2018.07.010

2. Hnatushenko, V., Hnatushenko, Vik., Kavats, A., & Shevchenko, V. (2015). Pansharpening technology of high resolution multispectral and panchromatic satellite images. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (4), 91-98.

3. Liu, Y., Wang, L., Cheng, J., Li, C., & Chen, X. (2020). Multi-focus image fusion: A Survey of the state of the art. Information Fusion, 64, 71-91. https://doi.org/10.1016/j.inffus.2020.06.013

4. Kashtan, V., & Hnatushenko, V. (2023). Deep Learning Technology for Automatic Burned Area Extraction Using Satellite High Spatial Resolution Images. Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2022. Advances in Intelligent Systems and Computing, 1246, 664-685. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_37

5. Panda, M. K., Parida, P., & Rout, D. K. (2024). A weight induced contrast map for infrared and visible image fusion. Computers and Electrical Engineering, 117, 109256. https://doi.org/10.1016/j.compeleceng.2024.109256

6. Qiu, X., Li, M., Zhang, L., & Yuan, X. (2019). Guided filter-based multi-focus image fusion through focus region detection. Signal Processing: Image Communication, 72, 35-46. https://doi.org/10.1016/j.image.2018.12.004

7. Zhang, H., Xu, H., Tian, X., Jiang, J., & Ma, J. (2021). Image fusion meets deep learning: A survey and perspective. Information Fusion, 76, 323-336. https://doi.org/10.1016/j.inffus.2021.06.008

8. Farid, M. S., Mahmood, A., & Al-Maadeed, S. A. (2019). Multi-focus image fusion using Content Adaptive Blurring. Information Fusion, 45, 96-112. https://doi.org/10.1016/j.inffus.2018.01.009

9. Liu, Y., Liu, S., & Wang, Z. (2015). Multi-focus image fusion with dense SIFT. Information Fusion, 23, 139-155. https://doi.org/10.1016/j.inffus.2014.05.004

10.      Abhyankar, M., Khaparde, A., & Deshmukh, V. (2016). Spatial domain decision based image fusion using superimposition. 2016 IEEE/ACIS 15 ^th International Conference on Computer and Information Science. IEEE. https://doi.org/10.1109/icis.2016.7550766

11.      Kashtan, V., & Hnatushenko, V. (2019). Computer technology of high resolution satellite image processing based on packet wavelet transform. International Workshop on Conflict Management in Global Information Networks CMiGIN 2019, Lviv, Ukraine, (pp. 370-380). Retrieved from https://ceur-ws.org/Vol-2588/paper31.pdf

12.      Liu, Y., Liu, S., & Wang, Z. (2015). A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion, 24, 147-164. https://doi.org/10.1016/j.inffus.2014.09.004

13.      Kashtan, V., & Hnatushenko, V. (2020). A Wavelet and HSV Pansharpening Technology of High Resolution Satellite Images. Intelligent Information Technologies & Systems of Information Security Intel, 2020, 67-76. Retrieved from https://ceur-ws.org/Vol-2623/paper7.pdf

14.      Merianos, I., & Mitianoudis, N. (2019). Multiple-Exposure Image Fusion for HDR Image Synthesis Using Learned Analysis Transformations. Journal of Imaging, 5(3), 32. https://doi.org/10.3390/jimaging5030032

15.      Martorell, O., Sbert, C., & Buades, A. (2019). Ghosting-free DCT based multi-exposure image fusion. Signal Processing: Image Communication, 78, 409-425. https://doi.org/10.1016/j.image.2019.07.020

16.      Lin, Chenyoukang, Liu, Tao, Wang, Zixi & Wang, Bo (2025). CTIUFuse: A CNN-Transformer-based Iterative Feature Universal Fusion Algorithm for Multimodal Images. Knowledge-Based Systems, 329, 114313. https://doi.org/10.1016/j.knosys.2025.114313

17.      Liu, Z., Zheng, Y., & Han, X.-H. (2021). Deep Unsupervised Fusion Learning for Hyperspectral Image Super Resolution. Sensors, 21(7), 2348. https://doi.org/10.3390/s21072348

18.      Lai, R., Li, Y., Guan, J., & Xiong, A. (2019). Multi-Scale Visual Attention Deep Convolutional Neural Network for Multi-Focus Image Fusion. IEEE Access, 7, 114385-114399. https://doi.org/10.1109/access.2019.2935006

19.      Shao, Z., & Cai, J. (2018). Remote Sensing Image Fusion With Deep Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1656-1669. https://doi.org/10.1109/jstars.2018.2805923

20.      Wang, J., Shao, Z., Huang, X., Lu, T., & Zhang, R. (2021). A Dual-Path Fusion Network for Pan-Sharpening. IEEE Transactions on Geoscience and Remote Sensing, 1-14. https://doi.org/10.1109/tgrs.2021.3090585

21.      Benzenati, T., Kessentini, Y., & Kallel, A. (2022). Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks. Expert Systems with Applications, 188, 115996. https://doi.org/10.1016/j.eswa.2021.115996

22.      Wang, Z., Li, X., Duan, H., & Zhang, X. (2022). A Self-supervised Residual Feature Learning Model for Multi-focus Image Fusion. IEEE Transactions on Image Processing, 1. https://doi.org/10.1109/tip.2022.3184250

23.      Liu, S., & Yang, L. (2022). BPDGAN: A GAN-Based Unsupervised Back Project Dense Network for Multi-Modal Medical Image Fusion. Entropy, 24(12), 1823. https://doi.org/10.3390/e24121823

24.      Nam, H. Le, Sonka, M., & Toor, F. (2023). A Quantum Optimization Method for Geometric Constrained Image Segmentation. Quantum Physics. https://doi.org/10.48550/arXiv.2310.20154

25.      Blekos, K., Brand, D., Ceschini, A., Chou, C.-H., Li, R.-H., Pandya, K., & Summer, A. (2024). A review on Quantum Approximate Optimization Algorithm and its variants. Physics Reports, 1068, 1-66. https://doi.org/10.1016/j.physrep.2024.03.002

26.      Kashtan, V. J., Hnatushenko, V. V., & Shedlovska, Y. I. (2017). Processing technology of multispectral remote sensing images. 2017 IEEE International Young Scientists’ Forum on Applied Physics and Engineering, Lviv, (pp. 17-20), October 2017. https://doi.org/10.1109/ysf.2017.8126647

27.      Zou, P. (2025). Multiscale quantum approximate optimization algorithm. Physical Review A, 111(1).  https://doi.org/10.1103/physreva.111.012427

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