A Polarimetric Decomposition Method for Ice in the Bohai Sea Using C-Band PolSAR Data
Bibliographic record
Abstract
In recent years, there has been an increased interest in using synthetic aperture radar (SAR) to detect and monitor sea ice in the Bohai Sea for protecting offshore exploration and supporting marine transport. Two important tasks are the classification of sea ice and the determination of sea ice thickness, which can be achieved by considering the specific scattering mechanisms of the different ice types. This paper describes a three-component scattering model to decompose polarimetric SAR (PolSAR) data of sea ice. The total backscatter is modeled as the incoherent summation of surface, double-bounce, volume, and residual components. The proposed model extends the volume scattering contribution of sea ice by considering transmission, extinction, and refraction effects. The model is validated using C-band Radarsat-2 quad-polarization data acquired over sea ice in the Bohai Sea. The results show that the proposed polarimetric decomposition approach helps to distinguish different ice types and offers a proxy for sea ice thickness.
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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) | 0.001 | 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) | 0.001 | 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".