Polarization Phase Difference Analysis for Selection of Persistent Scatterers in SAR Interferometry
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Bibliographic record
Abstract
In this letter, we propose a technique for selecting persistent scatterers (PSs) based on their polarization phase difference (PPD). We analyze a normalized PPD between HH and VV channels averaged over a temporal set of images and select pixels that demonstrate predominantly even or odd bounce scattering properties. We compare selected scatterers to PSs selected by applying an amplitude dispersion threshold as suggested by a standard PS interferometry (PSI) approach and show that both methods are complementary. However, the proposed approach can be potentially used on a small set of synthetic aperture radar (SAR) images, which can be beneficial in the early stage of data acquisition. We apply the proposed technique to produce a deformation map for the San Francisco region from six quad-pol RADARSAT-2 SAR images acquired during 2008-2009. The coverage and the precision of the produced deformation map are higher than if it was calculated with the standard PSI technique applied to the same data set.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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