Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

ABSTRACT

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification.

Acknowledgements

Creation of the Extreme Earth dataset was funded under the European Union’s Horizon 2020 research and innovation programme grant agreement 825258 “From Copernicus Big Data to Extreme Earth Analytics”. We thank the Extreme Earth project and MET Norway for making this dataset available to the sea ice community at large. This research is supported by the National Science Foundation under Grant No. 2026962. We thank the journal editors as well as three anonymous referees that helped us improve the manuscript during peer-review. The code we developed for this project will be made available at https://github.com/geohai/sea-ice-segment upon publication.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2023.2248560

Additional information

Funding

This research is supported by the National Science Foundation under Grant No. 2026962.