Incidence Angle Dependence of Texture Statistics From Sentinel-1 HH-Polarization Images of Winter Arctic Sea Ice
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Bibliographic record
Abstract
The proliferation of synthetic aperture radar (SAR) imagery, its accessibility in open platforms like Google Earth Engine, and the development of automated classification and geophysical information extraction algorithms have prompted the need for understanding the role of incidence angle (IA) on radar scattering mechanism, backscatter intensity, and classification accuracy. This letter demonstrates the dependence of image texture parameters on SAR IA, using Arctic landfast sea ice samples extracted from C-band frequency Sentinel-1 SAR scenes collected during the winter period. Gray-level cooccurrence matrix (GLCM) derived texture parameters, and occurrence texture parameters, derived from undeformed first-year sea ice and multi-year sea ice, which are dominated by surface and volume scattering mechanisms, respectively, were analyzed. All GLCM texture parameters were found to be dependent on IA, highlighting the need for consideration of angular dependence in texture parameters, particularly in the development of image classification and inversion algorithms utilizing them. Occurrence texture parameters showed negligible influence by IA; however, feature discrimination capability was also lost. Some GLCM parameters had similar angular dependencies for both sea ice types, suggesting that, in some cases, global image normalization approaches for texture may be applied to account for the IA effect.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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