Application of bootstrap techniques for the estimation of Target Decomposition parameters in RADAR polarimetry
Bibliographic record
Abstract
The precise estimation of the eigenvalues of PolSAR reponses is essential in the derivation of Target Decomposition parameters such as the Cloude-Pottier parameters (Entropy, Anisotropy and average angle Alpha). However, sample eigenvalues are strongly biased for small sample sizes leading to underestimated Entropy and overestimated Anisotropy values. In this paper, we investigate the use of a particular bootstrap technique for the correction of the bias. Bootstrap techniques are attractive because they can deal with very small sample sizes under minimal assumptions on the signal distribution. Here, we are using the jackknife bias correction technique which has been successfully applied to various signal processing problems. Monte-Carlo simulations reveal that the jackknife bias correction directly applied on the Cloude-Pottier parameters lead to better bias reduction.
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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".