Detection of First-Year and Multi-Year Sea Ice from Dual-Polarization SAR Images Under Cold Conditions
Bibliographic record
Abstract
This paper presents a new technique for automated detection of multi-year (MY) and first-year (FY) sea ice from RADARSAT-2 dual-polarization HH–HV ScanSAR Wide images under cold environmental conditions. The approach is applied to 2.05 km$\times2.05$km ($41 \times 41$pixels) spatial window in the situation where the area is labeled as ice by our recently introduced ice and open water detection approach. The probability of the presence of MY ice is modeled as a function of the two selected predictor parameters computed over each spatial window: the HV/HH polarization ratio and the standard deviation of the HV signal. The proposed MY ice probability model was built based on thousands of synthetic aperture radar (SAR) images and corresponding Canadian Ice Service (CIS) Image Analysis products covering the 2010–2016 time period, not including 2013. Our verification results for the independent testing subset for the year 2013 against the CIS Image Analysis products suggest that approximately 50% of pure MY and FY ice samples were classified with an accuracy of 98.2%. The incidence angle correction of HH and HV backscatter does not improve MY and FY ice detection results in the space of the selected predictor parameters. The proposed technique will be used as part of the Environment and Climate Change Canada Regional Ice-Ocean Prediction System in support of assimilation of ice thickness retrievals from Cryosat-2 and Soil Moisture and Ocean Salinity mission data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".