Adaptive Probability Thresholding in Automated Ice and Open Water Detection From RADARSAT-2 Images
Bibliographic record
Abstract
In this letter, we introduce adaptive probability thresholding in addition to our previously developed technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dual-polarization HH–HV images. Situations where the probability threshold needs to be modified were identified based on the analysis of misclassified ice and water samples when the static probability threshold of 0.95 is applied. We found that with the use of the proposed approach, the fraction of misclassified ice samples decreased from 0.98% to 0.24% and the fraction of misclassified water samples decreased from 0.35% to 0.09% in the most clean verification scenario against Canadian Ice Service Image Analysis pure ice and water data, while the fraction of correctly classified ice and water samples did not decrease appreciably, from 72.2% to 65.4%. The developed approach will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".