Characterization of hurricane eyes in RADARSAT-1 images with wavelet analysis
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
AbstractStriking examples of RADARSAT-1 synthetic aperture radar (SAR) images of hurricanes have been acquired over the past few years. The images show, with high resolution, the imprint of these storms on the ocean surface roughness, including structures associated with atmospheric processes such as boundary layer rolls, and details associated with the eye of the storm. In this paper, an image-processing procedure for quantitatively characterizing SAR images of hurricane eyes (HEs) is described. The procedure uses the edge detection properties of wavelets to estimate the scale and area of HEs. Procedures are also introduced to determine a reference ellipse, the location of the centre, and an elliptical index. All parameters are measured quantitatively and objectively. Provision of a universal characterization procedure for SAR images of HEs will promote the use of RADARSAT-1 SAR images for the study of hurricane morphology and dynamics.Des exemples saisissants d'images RADARSAT-1 d'ouragans ont été acquis au cours des dernières années. Ces images montrent, avec une grande résolution, les empreintes de ces tempêtes sur la rugosité de surface de l'océan, incluant des structures associées à des processus atmosphériques comme les rouleaux de couches limites et des détails associés à l'œil de l'ouragan. Dans cet article, on décrit une procédure de traitement d'image pour la caractérisation quantitative des yeux d'ouragans (HE) sur les images RSO. La procédure utilize les propriétés de détection de contours des ondelettes pour estimer l'échelle et l'étendue des HE. Des procédures sont aussi introduites pour déterminer une ellipse de référence, la localization du centre et un index elliptique. Tous les paramètres sont mesurés quantitativement et objectivement. La mise au point d'une procédure universelle de caractérisation des HE sur les images RSO favorisera l'utilization des images RSO de RADARSAT-1 dans le contexte de l'étude de la morphologie et de la dynamique des ouragans.[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 | 0.001 | 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 it