Interferometric SAR Extended Coherence Calculation Based on Fractional Lower Order Statistics
Bibliographic record
Abstract
A polarimetric synthetic aperture radar (SAR) coherence calculation method based on fractional lower order statistics (FLOS) was proposed in Bian's paper. In this letter, we apply this approach to the coherence calculation for interferometric SAR (InSAR) and provide a detailed analysis. An L-band InSAR data set is used to provide comparative results between the coherence derived in the traditional manner and that based on FLOS. In the areas around strong scatterers, the coherence is found to be biased due to the deviation of the statistical model from Gaussian when using the traditional coherence calculation. However, the coherence based on FLOS largely reduces this bias. From the experimental results using the InSAR data, we found that this method reduces the artifacts in the traditional coherence calculation method. The removal of bias due to sample estimation is also discussed.
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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 | 0.000 |
| 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) | 0.000 | 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".