Method for determining elements of urban infrastructure objects based on the results from air monitoring
Journal Title: Восточно-Европейский журнал передовых технологий - Year 2019, Vol 4, Issue 9
Abstract
<p>The study proposes a two-stage method for determination of elements of urban infrastructure objects in images made by air monitoring systems. The first stage implies determining the contours of objects in images. The advanced Canny method was selected as the contour determination method. We considered the main stages of the advanced Canny method for determination of contours of objects in images made by air monitoring systems. The application of the Hough transform at the second stage was proposed.</p><p>The paper reports features in the method for determination of elements of urban infrastructure in color images made by air monitoring systems. In contrast to known methods, the method takes into account features of formation of images made by air monitoring systems. It highlights color channels and marks out contours and geometric primitives in each color channel; it re-integrated color channels and determines elements of urban infrastructure objects in the space of an output image.</p><p>The study presents the results of applying the method for determination of elements of urban infrastructure objects in a standard color image acquired from an air monitoring system. We defined elements of urban infrastructure objects, such as roads, houses, streets, building elements and others, as an example.</p>A visual evaluation of the quality of processing of a typical color image made by an air monitoring system was performed. We calculated errors of the first kind and the second kind. It was established that application of a two-stage method for determination of elements of urban infrastructure objects in an image made by an air monitoring system improves the quality of processing of optoelectronic images. Moreover, errors of the first kind and the second kind in determination of elements of urban infrastructure objects reduced by 13 % on average.
Authors and Affiliations
Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Nataliia Lukova-Chuiko, Hennady Pevtsov, Yurii Sheviakov, Iryna Yuzova, Yevhen Drob, Olexander Tytarenko
Keywords
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- EP ID EP666994
- DOI 10.15587/1729-4061.2019.174576
- Views 70
- Downloads 0
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