Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis
Bibliographic record
Abstract
Using a case study approach, the utility of synergistic RADARSAT-2 (R2) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery is demonstrated for ocean feature signature analysis in the vicinity of the Gulf Stream. The R2 and S1 images considered are either spatially adjacent or spatially overlapping, and were quasi-simultaneously collected (i.e., within minutes of each other). Spatially adjacent R2 and S1 imagery allows ocean feature signatures to be delineated over large spatial areas, while spatially overlapping R2 and S1 imagery collected within short time intervals provides independent ‘looks’ at the same ocean features. This permits determination of the surface displacement of features, potentially leading to improved classification of the origin of ocean feature signatures (quasi-stationary features are likely related to sea surface temperature fronts, while mobile features are likely related to atmospheric conditions). Further, we demonstrate how the use of S1 Level-2 products (i.e. radial velocity datasets) can be leveraged as contextual data to improve the interpretation and classification of ocean feature signatures extracted from R2 imagery. Despite the straight-forward approach taken here, this work demonstrates that there are practical, real-world applications that would benefit from exploiting these on-going imaging opportunities in operational environments.
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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".