Analysis of consistency in first-year sea ice classification potential of C-band SAR polarimetric parameters
Bibliographic record
Abstract
AbstractThe consistency in first-year sea ice classification potential of C-band SAR polarimetric parameters was analyzed by comparing the results of two studies conducted for the same ice types under different geophysical settings. The SAR images used in the comparison were acquired at an incidence angle difference of 4°. Probability density functions, grey level parameter images, and classification statistics derived using k-means classifier were used in the comparative analysis. The investigation showed that not all polarimetric parameters exhibit consistency in their classification performance under different geophysical settings. Out of the 20 polarimetric parameters analyzed, 12 demonstrated high levels of classification consistency between the two studies. Among these 12 parameters, only four possessed high classification accuracy and could be applicable for sea ice classification under variable environmental conditions. The parameters that showed the highest classification accuracies in both the studies were found to be inconsistent in their ice type separation capabilities. The signatures of these parameters differed for one or more ice types when compared between the two studies. The utility of these parameters in individual sea ice classification studies is recommended but their relevance in generalized sea ice classification scheme is unclear.On analyse la cohérence des paramètres RSO polarimétriques en bande C dans le potentiel de classification de la glace de mer de première année en comparant les résultats de deux études menées sur les mêmes types de glace mais dans des environnements géophysiques différents. Les images RSO utilisées pour la comparaison ont été acquises avec une différence de 4° au niveau de l'angle d'incidence. Des fonctions de densité de probabilité, des images paramétriques en niveaux de gris et des statistiques de classification dérivées à l'aide d'un classifieur k-moyennes sont utilisées pour les besoins de l'analyse comparative. L'étude montre que tous les paramètres polarimétriques n'affichent pas une cohérence égale dans leur performance de classification dans des environnements géophysiques différents. Parmi les vingt paramètres polarimétriques analysés, douze ont montré un haut niveau de cohérence dans la classification entre les deux études. Parmi ces douze paramètres, seulement quatre affichaient une précision élevée de classification et pourraient être applicables à la classification de la glace de mer dans des conditions environnementales variables. Les paramètres qui ont donné les précisions de classification les plus élevées dans les deux études se sont avérés incohérents au niveau de leur potentiel de séparation des types de glace. Les signatures de ces paramètres différaient pour un type de glace ou plus en comparant les deux études. L'utilisation de ces paramètres dans les études individuelles de classification des types de glace de mer est recommandée quoique leur pertinence demeure incertaine dans les procédures générales de classification de la glace de mer.[Traduit par la Rédaction] AcknowledgementsThe authors would like to thank Dr. Randall Scharien, Mark Christopher Fuller. and the crew of Amundsen (CFL – 2008) for their logistical support and field assistance. The Canadian Ice Service is recognized for their technical support. We also acknowledge the financial support from Natural Sciences and Engineering Research Council of Canada (NSERC) in the form of a Discovery and Research Tools Grant and the NSERC NCE program ArcticNet for logistical and financial support. PCI Geomatica is recognized for their software support. RADARSAT-2 data is acquired through Canadian Space Agency, SOAR-Program. RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd., 2008 – All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.
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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".