COMPARATIVE ANALYSIS OF SPECTRAL ANOMALIES DETECTION METHODS ON IMAGES FROM ON-BOARD REMOTE SENSING SYSTEMS
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Abstract
The subject matter of the article is methods of detecting spectral anomalies on images from remote sensing systems. The goal is to conduct a comparative analysis of methods for detecting spectral anomalies on images from remote sensing systems. The tasks are: analysis of the main methods of detecting spectral anomalies on images from remote sensing systems; processing of images from remote sensing systems using basic methods of detecting spectral anomalies; comparative assessment of the quality of methods for detecting spectral anomalies on images from remote monitoring systems. The methods used are: methods of digital image processing, mathematical apparatus of matrix theory, methods of mathematical modeling, methods of optimization theory, analytical and empirical methods of image comparison. The following results are obtained. The main methods of detecting spectral anomalies on images from remote sensing systems were analyzed. Processing of images from remote sensing systems using the basic methods of detecting spectral anomalies was carried out. A comparative assessment of the quality of methods for detecting spectral anomalies on images from remote monitoring systems was carried out. Conclusions. The spectral difference of the considered methods is revealed by the value of information indicators - Euclidean distance, Mahalanobis distance, brightness contrast, and Kullback-Leibler information divergence. Mathematical modeling of the considered methods of detecting spectral anomalies of images with a relatively “simple” and complicated background was carried out. It was established that when searching for a spectral anomaly on an image with a complicated background, the method based on the Kullback-Leibler divergence can be more effective than the other considered methods, but is not optimal. When determining several areas of the image with high divergence indicators, they should be additionally investigated using the specified methods in order to more accurately determine the position of the spectral anomaly.
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Sharad, W. (2021), “The development of the earth remote sensing from satellite”, Mechanics of gyroscopic systems, No. 40 (2020), pp. 46–54, doi: https://doi.org/10.20535/0203-3771402020248768
Lafabregue, B., Gançarski, P., Weber, J. And Forestie, G. (2022), “Incremental constrained clustering with application to remote sensing images time series”, 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 814–823, doi: https://doi.org/10.1109/ICDMW58026.2022.00110
Eismann, M., Stocker, A. and Nasrabadi, N. (2009), “Automated hyperspectral cueing for civilian search and rescue”, 2009 Proceedings of the IEEE, vol. 97, no. 6, pp. 1031–1055, doi: https://doi.org/10.1109/JPROC.2009.2013561
Kumar, S., Kumar, A. and Lee, D.-G. (2022), “Semantic Segmentation of UAV Images Based on Transformer Framework with Context Information”, Mathematics, vol. 10, no. 24, 4735, doi: https://doi.org/10.3390/math10244735
Favorskaya, M. N. and Zotin, A. G. (2021), “Semantic segmentation of multispectral satellite images for land use analysis based on embedded information”, Procedia Computer Science, vol. 192, pp. 1504–1513, doi: https://doi.org/10.1016/j.procs.2021.08.154
Grosgeorge, D., Arbelot, M., Goupilleau, A., Ceillier, T. and Allioux R. (2020), “Concurrent segmentation and object detection CNNS for aircraft detection and identification in satellite images”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), doi: https://doi.org/10.48550/arXiv.2005.13215
Safarov, F., Temurbek, K., Jamoljon, D., Temur, O., Chedjou, J. C., Abdusalomov, A. B. and Cho, Y. I. (2022), “Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture”, Sensors, vol. 22, no. 24, 9784, doi: https://doi.org/10.3390/s22249784
Neupane, B., Horanont, T. and Aryal J. (2021), “Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis”, Remote Sensing, vol. 13, no. 4, 808, doi: https://doi.org/10.3390/rs13040808
Khudov, H., Khizhnyak, I., Glukhov, S., Shamrai, N. and Pavlii V. (2024), “The method for objects detection on satellite imagery based on the firefly algorithm”, Advanced Information Systems, vol. 8, no. 1, pp. 5–11. doi: https://doi.org/10.20998/2522-9052.2024.1.01
Borghys, D., Achard, V., Rotman, S.R., Gorelik, N., Perneel, C. and Schweicher E. (2011), “Hyperspectral anomaly detection: A comparative evaluation of methods”, XXXth URSI General Assembly and Scientific Symposium, IEEE, pp. 1–4, doi: https://doi.org/10.1109/URSIGASS.2011.6050650
Redei, G. (2016), Encyclopedia of Genetics, Genomics, Proteomics and Informatics, Springer, Dordrecht, 638 p., doi: https://doi.org/10.1007/978-1-4020-6754-9_5603
Mclachlan, P. (1999), Mahalanobis Distance / Resonance, vol. 4, pp. 20–26, doi: https://doi.org/10.1007/BF02834632
Reed, I.S. and Yu, X. (1990), “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38, no. 10, pp. 1760–1770, doi: https://doi.org/10.1109/29.60107
Kupchenko, L., Rybiak, A., Goorin, О. and Biesova, O. (2022), “Experimental researches of dynamic spectral processing of optical radiation in the active electro-optical system”, Semiconductor Physics, Quantum Electronics & Optoelectronics, vol. 25, no. 1, pp. 090–096, doi: https://doi.org/10.15407/spqeo25.01.090
Kupchenko, L., Khudov, H., Hurin, А., Rybiak, А. and Goorin O. (2022), “Improved method for detecting spectral anomalies based on the information criterion of Kulback-Leibler in remote sensing systems”, Weapon systems and military equipment, vol. 2, no. 70, pp. 56–61, doi: https://doi.org/10.30748/soivt.2022.70.07
Kullback, S. and Leibler, R.A. (1951), “On information and sufficiency”, The Annals of Mathematical Statistics, vol. 22, no. 1, pp. 79–86, doi: https://doi.org/10.1214/aoms/1177729694
Kupchenko, L. and Rybiak, А. (2015), “The matching criterion of optimal signal processing in electro-optical systems with dynamic spectral filtration”, Weapon systems and military equipment, vol. 1, no. 41, pp. 120–123. URL: https://www.hups.mil.gov.ua/periodic-app/article/2504
Kupchenko, L., Rybiak, А. and Goorin O. (2018), “Estimation of matching of optimal dynamic spectral filtration in electro-optical system of target detection”, Radiophysics and electronics, vol. 23, no. 1, pp. 42–52. doi: https://doi.org/10.15407/rej2018.01.042
Kupchenko, L., Khudov, H., Hurin, А., Rybiak, А. and Goorin, O. (2022), “Development of a method for detecting changes in the spectral structure of images using the information criterion – normalized Kullback-Leibler divergence”, Weapon systems and military equipment, 2022, vol. 1, no. 69, pp. 33–39, doi: https://doi.org/10.30748/soivt.2022.69.04
Gorokhovatskyi, V., Peredrii, O., Tvoroshenko, I. and Markov, T. (2023), “Distance matrix for a set of structural description components as a tool for image classifier creating”, Advanced Information Systems, vol. 7, no. 1, pp. 5–13, doi: http://dx.doi.org/10.20998/2522-9052.2023.1.01
Kuchuk, H., Kovalenko, A., Ibrahim, B.F. and Ruban, I. (2019), “Adaptive compression method for video information”, International Journal of Advanced Trends in Computer Science and Engineering, vol. 8(1), pp. 66–69, doi: http://dx.doi.org/10.30534/ijatcse/2019/1181.22019
Tedore, C. and Johnsen, S. (2017), “Using RGB displays to portray color realistic imagery to animal eyes”, Current Zoology, vol. 63, no. 1, pp. 27–34, doi: https://doi.org/10.1093/cz/zow076
Kovalenko, A. and Kuchuk, H. (2022), “Methods to Manage Data in Self-healing Systems”, Studies in Systems, Decision and Control, vol. 425, pp. 113–171, doi: https://doi.org/10.1007/978-3-030-96546-4_3
Svyrydov, A., Kuchuk, H. and Tsiapa, O. (2018), “Improving efficienty of image recognition process: Approach and case study”, Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 593–597, DOI: https://doi.org/10.1109/DESSERT.2018.8409201·
Fukunaga, K. (1990), Introduction to statistical pattern recognition, Academic Press, Inc., San Diego, 626 p., doi: https://doi.org/10.1016/C2009-0-27872-X