The article examines the problem of object identification based on thermal images in highly noisy atmospheric conditions, which is critical for various fields, including military applications, security, search and rescue operations, and industry. The primary focus is on analyzing modern methods for compensating for the effects of atmospheric factors such as fog, rain, dust, and temperature fluctuations, which significantly degrade the quality of thermal images. The study reviews key approaches to noise compensation, including image filtering techniques, mathematical heat transfer models, multispectral sensors, machine learning-based algorithms, and hybrid systems. Special emphasis is placed on In-Scene Atmospheric Correction and Kalman Filter Augmentation algorithms, which enable effective adaptation to changing conditions and ensure high accuracy in analysis. The main advantages and drawbacks of each method are discussed, with particular attention to their practical implementation.
- Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2021). Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 3973–3985. https://doi.org/10.1109/TGRS.2011.2128325
- Gao, B.-C., Montes, M. J., Davis, C. O., & Goetz, A. F. H. (2023). Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sensing of Environment, 113, S17–S24. https://doi.org/10.1016/j.rse.2007.12.015
- Gao, L., & Zhang, B. (2023). A novel method for automatic noise reduction in hyperspectral images using spectral derivatives. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2981–2987. https://doi.org/10.1109/TGRS.2009.2038905
- Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45. https://doi.org/10.1115/1.3662552
- Li, S., Kwok, J. T., & Wang, Y. (2022). Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Information Fusion, 3(1), 17–23. https://doi.org/10.1016/S1566-2535(01)00037-9
- Pashkov, R. A., & Balakhonova, N. O. (2019). Resolution of thermal imaging devices according to Johnson's criteria. A look into the future of instrumentation: materials of the XII All-Ukrainian scientific and practical conference of students, postgraduates and young scientists, Kyiv, May 5–16, 2019, 117–120.
- Pustovietov, V. M., & Dobrovolskyi, A. B. (2022). Research on the effectiveness of thermal imaging observation tools in different climatic conditions. Bulletin of Khmelnytskyi National University, 2(187), 205–207.
- Richards, J. A., & Jia, X. (2023). Remote sensing digital image analysis: An introduction (4th ed.). Springer.
- Rodgers, C. D. (2023). Inverse methods for atmospheric sounding: Theory and practice. World Scientific Publishing Company.
- Schott, J. R. (2022). Remote sensing: The image chain approach (2nd ed.). Oxford University Press.
- Young, S. J., Johnson, B. R., & Hackwell, J. A. (2022). An in-scene method for atmospheric compensation of thermal hyperspectral data. Journal of Geophysical Research: Atmospheres, 107(D24), ACH 16-1–ACH 16-14. https://doi.org/10.1029/2001JD001266
- Zhu, X., & Milanfar, P. (2024). Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Transactions on Image Processing, 19(12), 3116–3132. https://doi.org/10.1109/TIP.2010.2052823
- Vettori, S., Di Lorenzo, E., Peeters, B., Luczak, M. M., & Chatzi, E. (2023). An adaptive-noise augmented Kalman filter approach for input-state estimation in structural dynamics. Mechanical Systems and Signal Processing, 184, 109654. https://doi.org/10.1016/j.ymssp.2022.109654