Ship detection and characterization using polarimetric SAR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractPolarimetric information is investigated for ship detection and characterization at operational satellite synthetic aperture radar (SAR) incidence angles (20°‐60°). It is shown that among the conventional single-channel polarizations (HH, VV, or HV), HV provides the best ship‐sea contrast at incidence angles smaller than 50°. Furthermore, HH polarization permits the best ship‐sea contrast at near-grazing incidence angles. The wave polarization anisotropy is used for optimal information extraction from polarimetric SAR data. It is shown that fully polarimetric information permits a significant improvement in the ship‐sea contrast for relatively calm wind conditions, in comparison with conventional (i.e., scalar) single-channel polarizations (i.e., HH, VV, or HV). For rougher sea conditions, the effectiveness of polarimetric tools may be significantly degraded. Ship characterization is also investigated using the symmetric scattering characterization method (SSCM). Identification of ship targets with significant symmetric scattering can provide a useful ship pitch angle estimate under certain conditions. L'apport de l'information polarimétrique à la détection et la caractérisation des bateaux est étudiée. Parmi les polarisations conventionnelles HH, VV, et HV, la polarisation HV permet le meilleur contraste bateau-mer aux angles d'incidence plus petits que 50°. HH donne les meilleurs résultats aux incidences rasantes. L'anisotropie de polarisation a été utilizée pour l'extraction optimale de l'information polarimétrique. La polarimétrie permet une grande amélioration du contraste bateau‐mer dans des conditions de mer et vents relativement calmes. L'efficacité de la polarimétrie est réduite quand la mer est agitée. La méthode SSCM a été testée pour la caractérisation de bateaux. Elle a même permis une mesure de l'angle de tangage de bateaux dans certaines conditions.
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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 it