https://libeldoc.bsuir.by/handle/123456789/54316| Title: | Comparative Analysis of Semantic Segmentation Methods for Satellite Images Segmentation |
| Authors: | Qing Bu Wei Wan Savitskaya, E. |
| Keywords: | материалы конференций;semantic segmentation;image segmentation;urban scenes;deep neural network;U-Net;CNN-based semantic segmentation;transformers |
| Issue Date: | 2023 |
| Publisher: | BSU |
| 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. |
| 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. |
| URI: | https://libeldoc.bsuir.by/handle/123456789/54316 |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) |
| File | Description | Size | Format | |
|---|---|---|---|---|
| Qing_Bu_Comparative.pdf | 1.4 MB | Adobe PDF | View/Open |
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