D.V. Vasilieva1

1St. Petersburg State University of Aerospace Instrumentation (St. Petersburg, Russia)

1dolli.dina@mail.ru

The problems of timely detection of environmental disturbances caused by the consequences of various man-made disasters on land and on the water surface of water areas of seas and oceans are based on imperfect methods of control and monitoring of large spaces. Therefore, it is relevant to search for approaches for learning procedures used in the construction of monitoring systems.

The aim of article to consider approaches to the formation of feature spaces based on the processing of multiple-scale distributions of signals from drone cameras and the application of discrete series of continuous-time wavelets.

An approach to the formalization of the recognition task is proposed. The results of using discrete series of continuous-time wavelets are presented, which allows obtaining a result similar to the use of a multi-dimensional filter bank loaded on integrators. Results of a practical experiment are presented, which showed that increasing the dimensionality of feature vectors reduces their contrast. The directions of further research are formed. The presented results open new possibilities for realization of recognition systems at the software level.

Vasiliev D.V. Algorithm for ocean surface pollution detection based on the results of video surveillance image processing. Information-measuring and Control Systems. 2024. V. 22. № 4. P. 6−16. DOI: https://doi.org/10.18127/j20700814-202404-01 (in Russian)

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