Document Type
Article
Publication Date
8-3-2025
Department
Michigan Tech Research Institute
Abstract
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 Arctic surface water features. Our study proposes a modified U-Net encoder-decoder model for this task, optimized using the Nadam algorithm. Otsu thresholding was leveraged to rapidly generate 512 × 512-pixel patches for the U-Net, resulting in an efficient and automated training pipeline. Within this study, we also quantitatively compared the deep learning (DL) U-Net to a shallow machine learning (ML) algorithm, XGBoost (XGB), and evaluated the Capella Space imagery against spatially and temporally coincident Sentinel-1 C-band. Performance evaluations showed the U-Net outperforms XGB measured under several statistical metrics, reaching an Intersection over Union (IoU) of 0.955. An explainability analysis was conducted to complement this finding, using Gradient-weighted Class Activation Mapping (Grad-Cam). Visual analysis also underscored the extreme detail of small water features captured by Capella Space imagery, which are at times omitted or lack clarity in conventional Sentinel-1. This research makes several contributions to Arctic surface water mapping, demonstrating the effectiveness of combining VHR SAR imagery with DL.
Publication Title
Canadian Journal of Remote Sensing
Recommended Citation
Merchant, M.,
Mahdianpari, M.,
Bourgeau-Chavez, L.,
DeVries, B.,
&
Berg, A.
(2025).
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.
Canadian Journal of Remote Sensing,
51(1).
http://doi.org/10.1080/07038992.2025.2533460
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2038
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2025 the author(s). Published by Informa UK limited, trading as Taylor & Francis group. Publisher’s version of record: https://doi.org/10.1080/07038992.2025.2533460