Iceberg Detection Using Compact Polarimetric Synthetic Aperture Radar
Bibliographic record
Abstract
In recent years, the use of synthetic aperture radar (SAR) for iceberg detection has been increasing thanks to the greater availability and coverage of SAR data, particularly polarimetric data. Greater amounts of polarimetric information can increase detection performance, preventing false alarms and missed detections. However, quad-polarization (quad-pol) SAR systems have increased data rate and power usage requirements, causing quad-pol modes to have generally half the swath width of dual-polarization (dual-pol) modes. Compact polarimetry is a compromise that allows the approximation of quad-pol information (referred to as “pseudo quad-pol”) using a dual-pol SAR. In this paper, using compact polarimetric data simulated from RADARSAT-2 quad-pol imagery, we show how pseudo-quad-pol data can be used for iceberg detection using the likelihood ratio test method. We show that use of the pseudo-quad-pol HV intensity can augment the detection performance of a compact polarimetric SAR system. We also compare the performance of the various compact polarimetric configurations to linearly polarized dual-pol and quad-pol data. [Traduit par la rédaction] Ces dernières années, le radar à synthèse d'ouverture (RSO) a été de plus en plus utilisé pour la détection des icebergs grâce à une meilleure disponibilité et une meilleure couverture des données RSO, en particulier des données polarimétriques. De plus grandes quantités d'informations polarimétiques peuvent améliorer la performance de détection, ce qui évite les fausses alarmes et les détections manquées. Cependant, les systèmes RSO à quadruple polarisation (quad-pol) exigent des débits binaires et une consommation d’énergie plus élevés, de telle sorte que les modes quad-pol utilisent généralement une largeur de couloir la moitié moindre que celle des modes à double polarisation (dual-pol). La polarimétrie compacte est un compromis qui permet d'approximer l'information quad-pol (alors appelée « pseudo-quad-pol ») au moyen d'un RSO dual-pol. Dans cet article, en utilisant des données de polarimétrie compacte simulées à partir de l'imagerie RADARSAT-2 quad-pol, nous montrons comment les données pseudo-quad-pol peuvent servir à la détection des icebergs au moyen de la méthode du test du rapport des vraisemblances. Nous montrons que l'utilisation de l'intensité HV pseudo-quad-pol peut augmenter la performance de détection d'un système RSO polarimétrique compact. Nous comparons aussi la performance des diverses configurations polarimétriques compactes aux données dual-pol et quad-pol linéairement polarisées.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".