Improved ship detection with airborne polarimetric SAR data
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
AbstractThe ship-detection performance that can be obtained from polarimetric synthetic aperture radar (SAR) data is compared with that obtained from single-channel SAR data. Statistical decision theory is used to define decision variables that quantify the tradeoff between the probability of missed detection and the probability of false alarm; performance is characterized by calculating receiver operating characteristics from single-channel and polarimetric SAR data by using likelihood ratio tests with the Neyman–Pearson criterion. It is shown that ship-detection performance obtained with polarimetric SAR data is improved compared with that obtained with single-channel SAR data. We also evaluate the results of these algorithms when applied to single-channel, dual-channel amplitude-only, dual-channel with amplitude and phase, and fully polarimetric SAR data of known ships. In this way, the relative improvement in ship-detection performance that is realized by using polarimetric information is quantified.On compare les améliorations obtenues au niveau de la performance dans la détection des navires suite à l'utilisation des données polarimétriques radar à synthèse d'ouverture (RSO) comparativement aux données RSO à bande unique. La théorie de décision statistique est utilisée pour définir des variables de décision qui permettent de quantifier le compromis à faire entre la probabilité de détection ratée et la probabilité de fausse alarme; la performance est caractérisée en calculant les caractéristiques de fonctionnement du récepteur à partir de données RSO en bande unique et polarimétriques en utilisant des tests de ratio de probabilité avec le critère Neyman-Pearson. Il est démontré que l'on peut améliorer la performance au plan de la détection des navires en utilisant des données polarimétriques RSO comparativement aux données RSO en bande unique. Nous évaluons également les résultats de ces algorithmes appliqués à des données à bande unique, à deux bandes avec l'amplitude seulement, à deux bandes avec l'amplitude et la phase, et polarimétriques de navires connus. De cette façon, il est possible de quantifier l'amélioration relative de la performance dans la détection des navires réalisée en introduisant l'information polarimétrique.[Traduit par la Rédaction]
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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