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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/288229
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dc.contributor.authorYe, S.P.-
dc.contributor.authorChen, C.X.-
dc.contributor.authorNedzved, A.-
dc.contributor.authorJiang, J.-
dc.date.accessioned2022-11-02T06:49:35Z-
dc.date.available2022-11-02T06:49:35Z-
dc.date.issued2020-
dc.identifier.citationComput Opt 2020;44(6):944-950.ru
dc.identifier.urihttps://elib.bsu.by/handle/123456789/288229-
dc.description.abstractThe buildings are very complex for detection on SAR images, where the basic features of those are shadows. There are many different representations for SAR shadow. As result it is no possible to use convolutional neural network for building detection directly. In this article we give property analysis of SAR shadows of different type buildings. After that, each region (ROI) prepared for training of building detection is corrected with its own SAR shadow properties. Reconstructions of ROI will be put in a modified YOLO network for building detection with better quality result.ru
dc.language.isoenru
dc.publisherInstitution of Russian Academy of Sciencesru
dc.rightsinfo:eu-repo/semantics/openAccessru
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математикаru
dc.titleBuilding detection by local region features in SAR imagesru
dc.typearticleru
dc.rights.licenseCC BY 4.0ru
dc.identifier.DOI10.18287/2412-6179-CO-703-
dc.identifier.scopus85098732302-
Appears in Collections:Статьи факультета прикладной математики и информатики

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