Incidence Angle Dependence of Texture Features From Dual Polarization Radarsat-2 Sea Ice Imagery
Bibliographic record
Abstract
This study investigates the relationship between gray-level co-occurrence matrix (GLCM) texture features and synthetic aperture radar (SAR) incidence angle (IA) for sea ice classification. We analyzed dual polarization RADARSAT-2 C-band SAR data comprising 29 scenes. GLCM features were extracted from the radar cross-section (σo) in dB and categorized by sea ice class. To assess IA dependence per sea ice class, we used linear interpolation and the coefficient of determination (R2). We evaluated separability among ice classes using the Jeffries–Matusita distance and confirmed the improved separability with a Bayesian classifier. The results reveal a significant IA dependence of GLCM features. Notably, GLCM features from the HV band display stronger IA dependence and higher separability among ice classes compared to those from the HH band. These findings emphasize the significance of considering IA in the utilization of GLCM features for sea ice classification.
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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 | 0.003 |
| 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 | 0.001 | 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 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".