Oil Pollution Monitoring by the Radarsat Constellation Mission Compact Polarimetry
Bibliographic record
Abstract
The detection of oil spills in oceans has attracted considerable attention due to their adverse effects on marine ecosystems. Synthetic Aperture Radar (SAR) has emerged as a crucial tool for monitoring maritime pollution. Effective oil spill detection via SAR requires a minimal noise floor, extensive coverage, and polarization diversity to improve the identification and discrimination of pollution characteristics. The RADARSAT Constellation Mission (RCM) allows for the acquisition of Hybrid Polarimetric (HP) SAR imagery across all operational imaging modes. In this study, we investigate the effectiveness of a comprehensive set of CP features for detecting oil spills, focusing on the incident involving the bulk carrier MV Wakashio, which ran aground in July 2020 near the southeastern shores of Mauritius. Two SAR images were obtained over the experimental site using the RCM 16MCP imaging mode. Our results demonstrate that numerous CP features show promising capabilities in distinguishing various concentrations of oil spills.
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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) | 0.002 | 0.001 |
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".