Use of sequential SAR images for detecting ice and water in view of data assimilation
Bibliographic record
Abstract
In this study, we present a technique for automated detection of ice and open water based on ice motion information derived from sequential RADARSAT-2 images and an ice probability model applied to both SAR images. We investigate how the use of sequential synthetic aperture radar (SAR) images could increase the number of ice/water retrievals compared to the ice/water detection applied to a single SAR image only. The proposed technique was run for 736 image pairs and the ice/water retrieval results were verified against Canadian Ice Service Image Analysis products. Our results suggest that the fraction of water samples classified correctly has significantly increased from 65% (in the case of using a single SAR image) to 81% (in the case of using sequential SAR), while the detection accuracy stayed at approximately the same high level exceeding 99%. The developed approach is recommended to be implemented as part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System. The results are particularly important in light of the upcoming Canadian RADARSAT constellation mission which will significantly increase the amount and frequency of SAR observations over the Arctic region.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".