| DC Field | Value | Language |
|---|
| dc.contributor.author | Qing Bu | - |
| dc.contributor.author | Wei Wan | - |
| dc.contributor.author | Savitskaya, E. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-02-22T06:41:30Z | - |
| dc.date.available | 2024-02-22T06:41:30Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Qing Bu. Comparative Analysis of Semantic Segmentation Methods for Satellite Images Segmentation / Qing Bu, Wei Wan, E. Savitskaya // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 332–337. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54316 | - |
| dc.description.abstract | This paper proposes a comparative analysis of different automatic semantic segmentation methods for satellite
images segmentation on the Semantic Drone Dataset with 23 classes (paved-area, dirt, grass, gravel, water, rocks, pool, vegetation, roof, wall, window, door, fence, fence-pole, person, dog, car, bicycle, tree, bald-tree, ar-marker, obstacle, conflicting). We compare such models as U-net, U-net++, FPN, PAN, DeepLabV3, DeepLabV3+ and Transformer architecture model - SegFormer. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BSU | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | semantic segmentation | en_US |
| dc.subject | image segmentation | en_US |
| dc.subject | urban scenes | en_US |
| dc.subject | deep neural network | en_US |
| dc.subject | U-Net | en_US |
| dc.subject | CNN-based semantic segmentation | en_US |
| dc.subject | transformers | en_US |
| dc.title | Comparative Analysis of Semantic Segmentation Methods for Satellite Images Segmentation | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
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