Speckle Correction Filter Based on Spatial Polarimetric Coherence for Full Polarimetric Synthetic Aperture Radar Image
Bibliographic record
Abstract
Polarimetric synthetic aperture radar (PolSAR) image exhibits a high degree of spatial information. However, the PolSAR image is subjected to speckle noise, which degrades the information quality. A speckle correction filter is proposed in this paper based on spatial polarimetric coherence. Evidence of polarimetric coherence between spatial neighboring pixels is presented. It shows that polarimetric coherence is highly correlated with neighboring pixels. Based on these characteristics, a speckle correction filter is proposed by calculating the polarimetric coherency level on a selected neighboring pixel. A correction is applied to the pixel which has a low polarimetric coherence level. A generalized three-step adaptive filter is proposed, including neighbor selection, coherency criterion, and incoherent correction. The influence of the filter on scattering mechanisms is compared by using eigenvalue-based decomposition. Qualitatively, the result shows the proposed correction filter removes the outliers in the low entropy region. Quantitively the filter can suppress the speckle noise and its variance up to 19.08%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".