Automated Semantic Segmentation of Arctic Surface Water Features with Very-High Resolution Satellite X-Band Radar Imagery and U-Net Deep Learning: Segmentation sémantique automatisée des caractéristiques des eaux de surface de l’Arctique à partir d’images radar satellite en bande X à très haute résolution et à l’aide de l’apprentissage profond U-Net
Repeatable methods capable of quantifying Arctic surface water extent at high resolutions are important, but still require development. Here, we present a study using very-high resolution (VHR) X-band Synthetic Aperture Radar (SAR) imagery from Capella Space for fine-scale semantic segmentation of A...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Canadian Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/07038992.2025.2533460 |
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