An Enhanced Probabilistic Posterior Sampling Approach for Synthesizing SAR Imagery With Sea Ice and Oil Spills
Bibliographic record
Abstract
Although the synthesis of the synthetic aperture radar (SAR) imagery with both sea ice and oil spills can significantly benefit in improving the consistency and comprehensiveness of testing and evaluating algorithms that are designed for mapping cold ocean regions, creating such imagery is difficult due to the heterogeneity and complexity of the source images. This letter presents an enhanced region-based probabilistic posterior sampling approach to effectively synthesize SAR imagery with different ocean features. In the proposed approach, instead of relying entirely on the SAR intensity values, the posterior sampling is performed based on a number of quantitative factors, such as intensity, label field, and the prior class probability of sampling candidates, constituting a complete probabilistic framework that addresses key aspects in the synthesis of SAR imagery from heterogeneous sources. The experiments demonstrate that the proposed approach can better address the difficulties caused by the heterogeneity in the source images compared with the existing state-of-the-art ice synthesis method, and it will improve the consistency, comprehensiveness, and fairness of the evaluation of the remote sensing classification and segmentation algorithms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".