Sea Ice Motion Tracking From Sequential Dual-Polarization RADARSAT-2 Images
Bibliographic record
Abstract
A new sea ice motion tracking algorithm that operates with two sequential synthetic aperture radar (SAR) RADARSAT-2 ScanSAR images is presented. The feature tracking approach is based on the combination of the phase-correlation and cross-correlation methods. An algorithm for selecting control points, a matching technique, an approach for filtering out error vectors, and a confidence levels setting for output drift vectors were specifically developed in order to increase the system's robustness and accuracy. We evaluated ice motion tracking results derived from HH and HV channels of RADARSAT-2 ScanSAR imagery and formulated a condition where the HV channel is more reliable than the HH channel for ice tracking. Furthermore, we found that the ice motion tracking from the HV channel is not affected by noise floor stripes, which are prominent in the cross-polarization RADARSAT-2 ScanSAR images. The developed sea ice tracking technology was implemented at the Canadian Ice Service, Environment Canada for operational use. The system was successfully run to provide operational support of field work in the Arctic Ocean in compliance with the United Nations Convention on the Law of the Sea in the spring of 2010.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".