Automatic Detection of Ships in RADARSAT-1 SAR Imagery
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
RÉSUMÉNOAA/NESDIS a initié le programme "Alaska SAR Demonstration" dont l'objectif est de faire la démonstration du potentiel des images RSO en bande C de RADARSAT-1 à fournir une information utile et en temps opportun sur l'environnement et pour la gestion des ressources pour des utilisateurs en Alaska. Un des produits développés dans le cadre du programme est une liste de localisations des navires. Cet article décrit l'algorithme développé pour générer ce produit par le biais de la détection automatique des navires basée sur des changements dans les statistiques locales. À l'aide d'images à basse résolution (100 mètres d'espacement), on démontre que l'on peut détecter des navires de dimension supérieure à 35 mètres (représentant 105 navires sur un total de 272 dans la zone test) avec un taux de fausse alerte de 0,01% pour une seule détection. Avec des images à haute résolution (50 mètres d'espacement), on peut détecter des navires d'une dimension supérieure à 32 mètres (représentant 124 navires sur 272) avec un taux de fausse alerte de 0,002% pour une seule détection. L'algorithme est entièrement automatisé et prend environ 10 minutes de temps-machine pour traiter une image ScanSAR en mode B large.SUMMARYNOAA/NESDIS has initiated a program called the Alaska SAR Demonstration with the goal of demonstrating the utility of RADARSAT-1 C-band SAR imagery to provide useful, timely environmental and resource management information to users in Alaska. One product generated under the program is a list of ship locations. This paper describes the algorithm developed to generate this product by automatically detecting ships based on changes in the local statistics. Using low resolution imagery (100 metres sample spacing) it is shown that ships of lengths greater than 35 metres can be detected (representing 105 ships out of a total of 272 in the test set) with a false alarm rate of 0.01% for a single detection. With high-resolution imagery (50 metres sample spacing) ships of lengths greater than 32 metres can be detected (representing 124 ships out of 272) with a false alarm rate of 0.002% for a single detection. The algorithm is completely automated and takes approximately 10 minutes of elapsed time to run on a ScanSAR Wide B Mode image.View correction statement:Automatic Detection of Ships in RADARSAT-1 SAR Imagery Additional informationNotes on contributorsC.C. Wackerman• Christopher C. Wackerman is with Veridian ERIM International, P.O. Box 134008 Ann Arbor MI USA 48113-4008.K.S. Friedman• Karen S. Friedman, William G. Pichel, Pablo Clemente-Colón and Xiaofeng Li are with NOAA/NESDIS, WWBG, E/RA3, Room 102, 5200 Auth Rd., Camp Springs MD USA 20746-4304.W.G. Pichel• Karen S. Friedman, William G. Pichel, Pablo Clemente-Colón and Xiaofeng Li are with NOAA/NESDIS, WWBG, E/RA3, Room 102, 5200 Auth Rd., Camp Springs MD USA 20746-4304.P. Clemente-Colón• Karen S. Friedman, William G. Pichel, Pablo Clemente-Colón and Xiaofeng Li are with NOAA/NESDIS, WWBG, E/RA3, Room 102, 5200 Auth Rd., Camp Springs MD USA 20746-4304.X. Li• Karen S. Friedman, William G. Pichel, Pablo Clemente-Colón and Xiaofeng Li are with NOAA/NESDIS, WWBG, E/RA3, Room 102, 5200 Auth Rd., Camp Springs MD USA 20746-4304.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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