Potential of model-based polarimetric decomposition extended with multi-frequency and multi-incidence PolSAR observations
Bibliographic record
Abstract
This letter attempts to extend the model-based polarimetric decomposition (PD) theorems with multiple polarimetric synthetic aperture radar (PolSAR) observations. To consider both the surface and dihedral scattering depolarization effects, the X-Bragg model, the extended double Fresnel scattering model, and the step-wise volume scattering model are introduced in the model-based PD. The proposed methodology is explored by exploiting Airborne Synthetic Aperture Radar (AIRSAR) C-band and L-band data from southern Oklahoma, United States of America, and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric data from Winnipeg, Canada, with two flight lines of 31605-03 and 31606-03 in different incidence angles. The potential and superiority of the proposed decomposition method are examined by illustrating the false color composition images, and the power ratio statistics of surface, dihedral, and volume scattering components over selected bare soil, forest, grass, and urban patches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".