Polarization-Aware CrossGate U-Net for Sea Ice Classification
Bibliographic record
Abstract
Sea ice monitoring is crucial for climate studies, navigation safety, and sustainable management of Arctic regions. The introduction of the AI4Arctic Sea Ice Challenge dataset, which provides standardized multisource remote sensing data, has significantly facilitated the advancement of automated sea ice mapping techniques. This study introduces the Polarization-Aware CrossGate U-Net (PA-CG-U-Net) model with cross attention designed specifically for sea ice classification using this dataset. The proposed architecture integrates dual-polarization SAR images (HH and HV) and coarse-resolution microwave radiometric data (AMSR2) through separate encoders and cross-attention gates to capture polarization-specific features. The PA-CG-U-Net model reduced classification errors compared to the U-Net model and improved the segmentation accuracy for sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). Quantitative evaluations demonstrated consistent performance gains, with improved robustness indicated by lower variance across multiple training runs. Visual analyses further confirmed that PA-CG-U-Net produced less noisy classifications, which highlights the effectiveness of polarization-specific encoding and attention mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".