Abstract
Purposeor research is to develop an algorithm for detecting obstacles on the orthophotomap based on the analysis of the spectral landscape indices in the tasks of mobile robotic equipment navigation in agricultural areas.Methods.The following landscape indices characterizing objects of various types on a map obtained by spectral aerial photography have been considered in the paper: normalized difference vegetation index (NDVI), normalized building difference index (NDBI), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI). These indices provide an assessment of the four main classes of objects on the map: vegetation, buildings, water bodies, and soil cover. An algorithm that provides the segmentation of zones on the map which are impassable for ground robotic means using multispectral images and the considered indices was proposed.Results. Each image is presented in the form of a colour map based on the pixel-by-pixel calculation of the indicated indices. In this case, three indices, i.e. SAVI, NDWI, NDBI, are combined (superimposed on each other), and then the NDVI layer is subtracted from the resulting image to highlight the passable zones. Thus, a formula to obtain a mask of obstacles in the image was obtained. Hence, this algorithm allows generalizing the results of calculations for all selected indices and constructing a mask of obstacles in the image. For quantitative assessment the of the algorithm execution, the area of obstacles was calculated using the indices on a sample of manually marked images. The experiments conducted show that the developed algorithm provides, on average, detection of 85.47 % of the area of all impassable zones in the images in the above classes of land cover.Conclusion.An algorithm for the automated detection of obstacles on a map obtained from a spectral orthophotomap of the area for use in the tasks of mobile robotic equipment navigation in agricultural areas has been developed and tested. In the further research, to determine flat soil areas, it is planned to modify the developed solution using the improved modified soil-adjusted vegetation index (MSAVI).
Highlights
Purpose or research is to develop an algorithm for detecting obstacles on the orthophotomap based on the analysis
The following landscape indices characterizing objects of various types on a map obtained by spectral aerial photography have been considered in the paper
These indices provide an assessment of the four main classes of objects
Summary
Для сегментирования препятствий в работе был использован открытый источник для получения мультиспектральных изображений [23]. Для вычисления индексов SAVI, NDVI, NDWI, NDBI извлекались различные части из изображения 1. Рассчитанные индексы NDVI SAVI, NDWI, NDBI представлены в виде различных с наборов цветовых карт 3г отношения суммарной площади, занимаемой препятствиями, ко всей площапредставлена NDBI-карта местности, где черный цвет указывает наличие годи изображения. Использование индекса SAVI позволило определить в среднем 34%, NDBI – 26%, NDWI – 8%, а разработанный подход в среднем обеспечивает детектирование 85,47% от общей площади всех препятствий на изображении. Используя индекс SAVI для следующая последовательность действий: три индекса SAVI, NDWI, NDBI определения ровных грунтовых участков, так же, как и траву для движения совмещаются (накладываются друг на друга), а затем отнимается NDVI слой, РТК, но на данном этапе работы это невозможно из-за низкого качества мульчтобы выделить проходимые зоны. Используя индекс МSAVI для определения ровных грунтовых участков, однако для этого требуются мультиспектральные изображения более высокого качества
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