Sea Ice Detection from RADARSAT-2 Quad-Polarization SAR Imagery Based on Co- and Cross-Polarization Ratio
Bibliographic record
Abstract
Arctic sea ice detection is very important in global climate research, Arctic ecosystem protection, ship navigation and human activities. In this paper, by combining the co-pol ratio (HH/VV) and two kinds of cross-pol ratio (HV/VV, HV/HH), a novel sea ice detection method is proposed based on RADARSAT-2 quad-polarization synthetic aperture radar (SAR) images. Experimental results suggest that the co-pol ratio shows promising capability in sea ice detection at a wide range of incidence angles (25–50°), while the two kinds of cross-pol ratio are more applicable to sea ice detection at small incidence angles (20–35°). When incidence angles exceed 35°, wind conditions have a great effect on the performance of the cross-pol ratio. Our method is validated by comparison with the visual interpretation results. The overall accuracy is 96%, far higher than that of single polarization ratio (PR) parameter-based methods. Our method is suitable for sea ice detection in complex sea ice and wind conditions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".