POLARIMETRIC SIGNATURES IDENTIFICATION FOR DIFFERENT FEATURES IN RADARSAT-2 POLSAR IMAGE: A CASE STUDY OF HALAYIB AREA, EGYPT
Bibliographic record
Abstract
In fully polarized SAR (PolSAR) data the returned signal from a target contains all polarizations. More information about this target may be inferred with respect to single-polarization. Distinct polarization separates targets due to its different backscattering responses. A Radarsat-2 PolSAR image acquired on December 2013 of part of Halayib area (Egypt) was used in this study. Polarimetric signatures for various features (Wadi deposits, Tonalite, Chlorite schist, and Radar penetrated areas) were derived and identified. Their Co-polarized and Cross-polarized signatures were generated, based on the calculation of the backscattered power at various ellipticity and orientation angles. Graphical 3D-representation of these features was provided and more details of their physical information are depicted according to their different polarization bases. The results illustrate that polarimetric signatures, obtained due to factors like surface roughness, dielectric constant and feature orientation, can be an effective representation for analyzing various features. The shape of the signature is significant and can also indicate the scattering mechanisms dominating the features response.
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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 | 0.000 | 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 | 0.001 | 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".