| DC Field | Value | Language |
|---|
| dc.contributor.author | Kypriyanava, D. | - |
| dc.contributor.author | Pertsau, D. | - |
| dc.contributor.author | Tatur, M. | - |
| dc.coverage.spatial | Slovakia | en_US |
| dc.date.accessioned | 2023-11-13T06:06:39Z | - |
| dc.date.available | 2023-11-13T06:06:39Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Kypriyanava, D. Image Segmentation Approaches Applied for the Earth's Surface / D. Kypriyanava, D. Pertsau, M. Tatur // Central European Researchers Journal. – 2023. – Vol. 9, issue 1. – P. 13–19. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/53569 | - |
| dc.description.abstract | An analytical review of papers about remote sensing, as well as semantic segmentation and
classification methods to process these data, is carried out. Approaches such as template matching-based methods,machine learning and neural networks, as well as the application of knowledge about the analyzed objects are considered. The features of vegetation indices usage for data segmentation by satellite images are considered.Advantages and disadvantages are noted. Recommendations operations for a more accurate classification of thedetected areas on the sequence are given | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Faculty of Management Science and Informatics, University of Zilina | en_US |
| dc.subject | публикации ученых | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | semantic segmentation | en_US |
| dc.subject | remote sensing | en_US |
| dc.subject | vegetation indices | en_US |
| dc.title | Image Segmentation Approaches Applied for the Earth's Surface | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Публикации в зарубежных изданиях
|
