The Application of RADARSAT-2 Quad-Polarized Data for Oil Slick Characterization
Bibliographic record
Abstract
ABSTRACT Spaceborne radar data has been extensively used to monitor numerous oil spills worldwide. The radar imagery provides information on the spatial extent of the oil, but in general, there is limited information on the characteristics of the oil such as the discrimination of sheen from emulsion. The full polarimetry capabilities of RADARSAT-2 were investigated in this study using acquisitions collected over the Gulf of Mexico. In this study, the Cloude-Pottier target decomposition algorithm was used to extract polarimetric information from RADARSAT-2 quad-polarized images acquired over the Macondo oil spill in the Gulf of Mexico. The Cloude-Pottier entropy (H) provides a measure of the amount of mixing between scattering mechanisms. For a wind-roughened ocean surface, the scattering is dominated by a single dominant scattering mechanism, namely Bragg scattering (H → 0). In the presence of an oil slick, however, the entropy increases (H → 1) which is due to the number independent scattering mechanisms increasing due to damping of the small-scale Bragg waves. Comparison of entropy with the over flight observations indicated that the variability of the entropy was consistent with the variability of the oil properties suggesting that the entropy was providing a qualitative measure of the oil characteristics. Specifically, when there was open water and a thin sheen, the entropy was close to 0, but in the presence thicker oil due to the presence of, for example, an emulsion, the entropy had values that were close to 1.
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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".