Analysis of Orientation Angle Shifts on the Polarimetric Data Using Radarsat2 Images
Bibliographic record
Abstract
Polarization orientation angle (POA) shifts induced by the variations of range and azimuth slopes cause the polarization to rotate about the radar line of sight. Their existence reduces the accuracy measurement of geophysical parameters from polarimetric synthetic aperture radar (PolSAR) images and may generate erroneous scattering characteristics that could be misinterpreted. In real situations, terrain slopes rotate the polarization basis of the polarimetric scattering matrices by an orientation angle shift, and induce significant cross-polarization power. Consequently, it is desirable to compensate the data for the orientation effect before applying scattering model-based decompositions. In this paper, we investigate the compensation effect on the elements of the coherency matrix using the orientation angle extracted from circular polarization technique and from the copolarization signature applied to building areas. The effect of this compensation is that the volume scattering power is consistently decreased, while the double-bounce power is increased. The surface and helix scattering powers are roll invariant. Comparing both methods, we find that the circular polarization algorithm gives mostly the best results except for some targets. In this way, a combined use of both algorithms has been proposed to choose an optimum orientation angle, which can be used directly to compensate POLSAR data to ensure improvement in the overall polarimetric decomposition and classification. We illustrate our results using the polarimetric SAR images acquired on the Algiers city by the RadarSAT2 (FQ19) in C-band.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".