Using the Pauli Scattering Mechanisms for Analysis of Polarimetric SAR Images from RADARSAT Constellation and TanDEM-X Missions
Bibliographic record
Abstract
This work describes techniques for extraction of information in polarimetric images from RADARSAT constellation mission (RCM) and TanDEM-X (TDX) and TerraSAR-X (TSX) satellites. Using the boundary conditions of Maxwell's equations it is shown how the single bounce and double bounce scattering can be extracted from quad-pol and compact polarimetric (CP) SAR. We develop new equations to calculate the Pauli decomposition from simulated CP data using RCM quad-pol data. The RCM CP option is circular transmit, linear receive (CTLR). We find that using quad-pol data performs better than using CP data for Pauli decomposition, especially extraction of the HV component. Estimation of single bounce and double bounce scattering from RCM CP data is feasible and yields more information about scattering properties than the CP components alone. We show how more information can be extracted from bistatic TSX/TDX quad-pol data by estimation of the coherence of the different Pauli components and optimization of the interferometric coherence using ships as detection objects. This is a unique opportunity to use optimized coherence on moving ships for characterizing their structure.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".