Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction
Bibliographic record
Abstract
This study proposes a new metric to process dual-polarimetric coherent and incoherent synthetic aperture radar (SAR) data for coastline extraction purposes. The metric, based on the correlation between co- and cross-polarized channels, allows discriminating land from sea in an unsupervised way. Then, simple image processing is adopted to extract continuous coastline from the binary image. Experiments, undertaken on multipolarization C- (RadarSAT-2 and Sentinel-1) and X-band (Cosmo-SkyMed) SAR data collected in South of Italy together with Global Positioning System ground truth, confirm the soundness of the method which is shown to be both effective (a whole SAR scene is processed in seconds) and accurate (the mean error is less than $5$ and $7$ pixels for RadarSAT-2 and CosmoSkyMed, respectively).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".