Improved PSP and U-Net architectures for forest segmentation in remote sensing pictures
| dc.contributor.author | Slyusar, V. I. | |
| dc.contributor.author | Sliusar, I. I. | |
| dc.contributor.author | Pavlenko, A. | |
| dc.date.accessioned | 2023-08-30T18:16:00Z | |
| dc.date.available | 2023-08-30T18:16:00Z | |
| dc.date.issued | 2022-11-14 | |
| dc.description | Slyusar V., Sliusar I., Pavlenko A. Improved PSP and U-Net architectures for forest segmentation in remote sensing pictures.// IEEE 2nd Ukrainian Microwave Week (Virtual Event) IEEE UkrMW-2022, 14 – 18 November, 2022. - V. N. Karazin Kharkiv National University, Kharkiv, Ukraine. – 4 p. DOI: 10.1109/UkrMW58013.2022.10037105. | |
| dc.description.abstract | Various PSP and U-Net architectures for forest segmentation in remote sensing pictures have been proposed and investigated. The main improvements of the proposed architectures are based on using the BathNormalization layers, replacing MaxPool2D layers with AveragePooling2D, changing Conv2DTranspose to UpSampling2D blocks, etc. For the training of neural networks was used modified dataset of 128x128 pictures based on the dataset from Kaggle. As a result of improving architecture was given the maximum segmentation accuracy of 80.8 % on the validation set of pictures. | |
| dc.identifier.other | https://doi.org/10.1109/UkrMW58013.2022.10037105 | |
| dc.identifier.uri | https://dspace.pdau.edu.ua/handle/123456789/15236 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.title | Improved PSP and U-Net architectures for forest segmentation in remote sensing pictures | |
| dc.type | Thesis |