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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".