Abstract
This paper explores the application of a singular expanding path of Frequency Attention U-Net (FAUNet), specifically employing its frequency attention mechanism for road detection in remote sensing. Contrasting with the full dual-path architecture of the recently proposed FAUNet, this study cap-italizes on only the high-frequency attentive path, tailored for edge detection in road segmentation tasks. By focusing on this single path, the modified FAUNet is adept at highlighting the intricate details necessary for accurate road boundary identification in high resolution remote sensing images. Comparative evaluations are conducted against traditional models like U-Net, U-Net++, and a generic CNN under consistent experimental conditions, including identical datasets, loss functions, and training loops.
Original language | English |
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Title of host publication | IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 9609-9613 |
Number of pages | 5 |
ISBN (Electronic) | 9798350360325 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece Duration: 7 Jul 2024 → 12 Jul 2024 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Conference
Conference | 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 |
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Country/Territory | Greece |
City | Athens |
Period | 7/07/24 → 12/07/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Attention
- Edge detection
- Frequency Attention
- Road segmentation
- U-Net