Geodesic Distance Based Scattering Power Decomposition for Compact Polarimetric SAR Data
Bibliographic record
Abstract
We propose a geodesic distance based scattering power decomposition for compact polarimetric (CP) synthetic aperture radar (SAR) data acquired over agricultural landscapes. The proposed technique decomposes the polarized portion of the total backscattered power in proportion to the normalized target similarity measures. The measures are derived from the geodesic distances, which are computed between the Kennaugh matrices of observed and canonical targets (dihedral or trihedral). We observed a pseudo power component in the double bounce power, which can be attributed to target irregularities. In order to compensate for the pseudo power component, we proposed a compensation strategy by utilizing the CP radar vegetation index (CpRV I). The compensation factor assisted in readjusting the polarized power components. The proposed approach was tested with real (RADARSAT Constellation Mission: RCM) and simulated (RADARSAT-2: RS2) hybrid CP data over agricultural sites in Canada. The effectiveness of the approach was demonstrated by comparing the decomposed powers with a recently proposed CP scattering power decomposition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".