Detection of crashed aircraft in polarimetric imagery
Bibliographic record
Abstract
AbstractThis manuscript presents studies examining the use of C-band polarimetric Synthetic Aperture Radar (SAR) systems for the detection of crashed aircraft. The ultimate aim is to assist Search and Rescue in Canada in the location of such targets. Detection methodologies based on the Polarimetric Whitening Filter, Cameron Decomposition, and measures of even bounce contributions to the backscatter have been examined. Tests were performed using imagery of serviceable and crashed aircraft and crashed aircraft parts. Although individual methods make it possible to detect the crashed aircraft, best results for target detection with decreased numbers of false alarms occur when these methods are used in combination. La présente étude porte sur la détection des avions écrasés à l'aide de données radar à synthèse d'ouverture (RSO) acquises en bande C. L'objectif opérationnel est de supporté les efforts de recherche et sauvetage au Canada, à la localisation des sites d'écrasement d'avion. Les méthodes de détection par filtre polarimétrique du bruit blanc, par la décomposition de Cameron ainsi que par la mesure de la diffusion paire ont été analysées. Les sites d'études imagés comprenaient diverses cibles ponctuelles, dont des avions de service, des avions écrasés et des sections d'avion abîmées. Malgré la possibilité de détection des cibles par ces méthodes prises individuellement, la combinaison des algorithmes de détection accroît le potentiel de détection en réduisant le nombre de fausses alarmes.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".