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MORSKIE INTELLEKTUAL`NYE TEHNOLOGII
Article . 2020 . Peer-reviewed
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Processing and analysis of underwater multispectral images

Обработка и анализ подводных спектрозональных изображений

Processing and analysis of underwater multispectral images

Abstract

Одним из приоритетных направлений эффективного освоения водного пространства на сегодня можно обозначить поиск и обнаружение различных объектов, расположенных в прибрежных и глубоководных областях морских бассейнов, в частности залежей полезных ископаемых и минералов. Решение данной задачи возможно путем обработки и анализа изображений морского дна, с использованием различных алгоритмических и математических методов, реализованных в форме программ и приложений, исполняемых на современных вычислительных машинах и персональных компьютерах. Одним из путей реализации проектов, направленных на решение подобной задачи может стать проектирование необитаемых подводных аппаратов (НПА), которые будут выступать в качестве средства получения изображений высокого качества, используемых для последующей обработки с использованием соответствующего программного обеспечения. В статье приведена процедура обработки сложно-структурных (текстурных) изображений залежей полезных ископаемых на фоне морского дна, показана эффективность использования спектральных и цветовых параметров исследуемых изображений в задачах распознавания и измерения характеристик этих залежей. Процедура обработки, реализованная на базе анализа цветового содержания сюжетов и разработанное программное обеспечение НПА, позволили идентифицировать наличие выходов минералов на морском дне, а также оценить их пространственные (геометрические) характеристики, в частности, площадь. Апробация разработанной программы была проведена на искусственно созданных модельных изображениях, а также на реальных подводных изображениях полезных ископаемых. One of the priorities in today's effective global Ocean exploration can be stated, such as designing the methods of search and detection varieties of objects located in the coastal or deep-sea areas of marine basins, one of such objects can for example be minerals scattered along the sea floor. Accomplishment of this task is possible through processing and analyzing images of the seabed, using various algorithmic and mathematical methods implemented in the form of programs and applications which will be executed using computing devices. One of the ways of implementing such projects might become a designing an uninhabited underwater vehicle (UUV), which will act as a means of obtaining high-quality images used for subsequent processing in appropriate software. The current article contains description of operations of analyzing and processing structural images depicting minerals scattered along the sea floor. It shows the level of efficiency in use of spectral and color matching parameters applied to task of recognition and measurement underwater minerals on the examined images. Processing procedure and its software implementation, completed using the analysis of assemblies and its color content, allowed us to identify layout of the minerals along the sea floor, as well as estimate its spatial (geometric) properties such as amount of its quantity. Testing of the developed application was performed over the simulated images, as well as real images depicting underwater mineral clusters.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
bronze