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DOI: 10.17586/1023-5086-2022-89-10-68-79
УДК: 519.688
Evaluating and testing neural-network algorithm capabilities for automating image data analysis for remote sensing of the Earth
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Publication in Journal of Optical Technology
Мингалев А.В., Белов А.В., Габдуллин И.М., Марданова Д.А., Агафонова Р.Р., Шушарин С.Н., Шлеймович М.П. Оценка и тестирование возможностей нейросетевых алгоритмов для обеспечения автоматизации дешифрирования графической информации в комплексах дистанционного зондирования Земли // Оптический журнал. 2022. Т. 89. № 10. С. 68–79. http://doi.org/10.17586/1023-5086-2022-89-10-68-79
Mingalev A.V., Belov A.V., Gabdullin I.M., Mardanova D.A., Agafonova R.R., Shusharin S.N., Shleimovich M.P. Evaluating and testing neural-network algorithm capabilities for automating image data analysis for remote sensing of the Earth [in Russian] // Opticheskii Zhurnal. 2022. V. 89. № 10. P. 68–79. http://doi.org/10.17586/1023-5086-2022-89-10-68-79
A. V. Mingalev, A. V. Belov, I. M. Gabdullin, D. A. Mardanova, R. R. Agafonova, S. N. Shusharin, and M. P. Shleimovich, "Evaluating and testing neural-network algorithm capabilities for automating image data analysis for remote sensing of the Earth," Journal of Optical Technology. 89(10), 607-614 (2022). https://doi.org/10.1364/JOT.89.000607
Subject of study. Results of comparative analysis and testing of the application capabilities of several neural network detection algorithms, programming interfaces, and machine learning libraries for real-time analysis of graphical data from scanning thermal imaging surveying systems are presented. Method. The availability of programming interfaces for integration and adaptation of algorithms into the developed software, data processing rate, and object detection accuracy were selected as the main criteria for assessing the detection algorithms. These criteria were evaluated using practical experiments involving training and running neural network algorithms on test software using computers with different configurations. Main results. Modern neural network algorithms were demonstrated to enable image data processing with detection accuracy for specified object classes, which is sufficient for the automation of image recognition aimed at real-time processing of images from scanning thermal imaging surveying systems. Practical significance. The results of the investigation and tests presented in this study can be advantageous and reduce the time required for developers to find base neural network algorithms suitable for a practical implementation of automation aimed at image processing. Implementation of the considered algorithms in the developed software enables image analysis and processing in real time during surveillance owing to a reduction in the amount of data processed by the operator, thus enabling the removal of the post-processing stage from the technological sequence of the surveillance.
neural network algorithms, object detection, machine learning, scanner thermal imaging systems
OCIS codes: 150.0150, 100.0100
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