Improved Retrieval of Ice and Open Water From Sequential RADARSAT-2 Images
Bibliographic record
Abstract
In this paper, we present a new technique for automated detection of ice and open water from sequential RADARSAT-2 ScanSAR dual-polarization HH-HV images. The technique is based on combining a previously developed approach to ice and water detection applied to single synthetic aperture radar (SAR) images with the ice motion information derived from sequential SAR images. To evaluate the new approach, it was applied to 736 SAR image pairs acquired in 2013. Compared with the previous approach, the new approach produced an increase in the fraction of correctly classified water samples from 57.7% to 72.6% while the fraction of correctly classified ice samples did not change appreciably. The overall accuracy stayed at a high level exceeding 99.8%, when compared against the Canadian Ice Service Image Analysis pure ice and water samples. Verification results for different regions and months showed that the detection accuracy exceeds 99.5% for the most regions and months. The proposed approach can also assign enhanced quality to ice and water retrievals found in the reference image. The results are particularly relevant 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 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".