Optimal Compact Polarimetric Parameters and Texture Features for Discriminating Sea Ice Types during Winter and Advanced Melt
Bibliographic record
Abstract
C-band synthetic aperture radar (SAR) is widely used for sea ice monitoring and operational activities. The RADARSAT Constellation Mission (RCM), with its anticipated launch in 2018, will provide hybrid compact polarimetric (CP) C-band SAR data offering near-polarimetric capabilities at large image acquisition widths suitable for achieving operational and scientific objectives in the Arctic. Although C-band SAR is effective for sea ice monitoring, it is difficult to implement during advanced melt, when the sea ice cover is melting and covered by melt ponds. Ice type separability during winter (pre-melt) and advanced melt conditions was assessed using Kolmogorov–Smirnov statistical separability analyses and Support Vector Machine supervised classifications of RCM parameters simulated from 2 winter and 2 advanced melt RADARSAT-2 scenes. Through a detailed analysis of the 2 advanced melt scenes, it was found that the steep incidence angle (22.3–24.2°) simulated RCM CP parameters provide improved ice type separability during the advanced melt period compared with shallow incidence angles (39.6–42.2°). With respect to classification, an overall accuracy of 77.06% was found for a scene comprising first-year and multiyear ice types, and a higher overall accuracy of 85.91% was achieved by including gray level co-occurrence matrix parameters in the classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".