Classification of earth surface image segmentation methods
The classification of methods for land surface image segmentation is presented in the paper. Such approaches as template matching, machine learning and deep neural networks, as well as application of knowledge about analyzed objects are considered. Peculiarities of vegetation indices application for...
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| Format: | Article |
| Language: | English |
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Belarusian National Technical University
2024-01-01
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| Series: | Системный анализ и прикладная информатика |
| Subjects: | |
| Online Access: | https://sapi.bntu.by/jour/article/view/642 |
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| _version_ | 1832557367528521728 |
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| author | D. V. Kypriyanava D. Y. Pertsau M. M. Tatur |
| author_facet | D. V. Kypriyanava D. Y. Pertsau M. M. Tatur |
| author_sort | D. V. Kypriyanava |
| collection | DOAJ |
| description | The classification of methods for land surface image segmentation is presented in the paper. Such approaches as template matching, machine learning and deep neural networks, as well as application of knowledge about analyzed objects are considered. Peculiarities of vegetation indices application for satellite images data segmentation are considered. Advantages and disadvantages are noted. The results obtained by the authors of the methods that have appeared over the last 10 years are systematized, which will allow those interested to get oriented faster and form ideas for further research. |
| format | Article |
| id | doaj-art-4bcdc4a097d74194a96cbb423568af13 |
| institution | Kabale University |
| issn | 2309-4923 2414-0481 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Системный анализ и прикладная информатика |
| spelling | doaj-art-4bcdc4a097d74194a96cbb423568af132025-02-03T05:16:54ZengBelarusian National Technical UniversityСистемный анализ и прикладная информатика2309-49232414-04812024-01-0104202810.21122/2309-4923-2023-4-20-28473Classification of earth surface image segmentation methodsD. V. Kypriyanava0D. Y. Pertsau1M. M. Tatur2Belarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsThe classification of methods for land surface image segmentation is presented in the paper. Such approaches as template matching, machine learning and deep neural networks, as well as application of knowledge about analyzed objects are considered. Peculiarities of vegetation indices application for satellite images data segmentation are considered. Advantages and disadvantages are noted. The results obtained by the authors of the methods that have appeared over the last 10 years are systematized, which will allow those interested to get oriented faster and form ideas for further research.https://sapi.bntu.by/jour/article/view/642remote sensingmachine learningsemantic segmentationvegetation indices |
| spellingShingle | D. V. Kypriyanava D. Y. Pertsau M. M. Tatur Classification of earth surface image segmentation methods Системный анализ и прикладная информатика remote sensing machine learning semantic segmentation vegetation indices |
| title | Classification of earth surface image segmentation methods |
| title_full | Classification of earth surface image segmentation methods |
| title_fullStr | Classification of earth surface image segmentation methods |
| title_full_unstemmed | Classification of earth surface image segmentation methods |
| title_short | Classification of earth surface image segmentation methods |
| title_sort | classification of earth surface image segmentation methods |
| topic | remote sensing machine learning semantic segmentation vegetation indices |
| url | https://sapi.bntu.by/jour/article/view/642 |
| work_keys_str_mv | AT dvkypriyanava classificationofearthsurfaceimagesegmentationmethods AT dypertsau classificationofearthsurfaceimagesegmentationmethods AT mmtatur classificationofearthsurfaceimagesegmentationmethods |