Interferometric SAR Phase Filtering in the Wavelet Domain Using Simultaneous Detection and Estimation
Bibliographic record
Abstract
In this paper, two interferometric SAR (InSAR) phase-filtering methods are proposed. These methods are performed in the wavelet domain and employ the simultaneous detection and estimation technique. In the wavelet domain, closed-form estimator and detector equations are derived, based upon a quadratic cost function, to minimize the combined risk of detection and estimation and, thus, the least square errors. Both methods occur within the wavelet domain; however, the first method employs the wavelet packet, while the second method is performed in the undecimated wavelet domain. A major characteristic of InSAR phase data is that the noise level is spatially variable, and the proposed methods have a particularly good comparative performance in these situations. Tests are performed using simulated phase data and show that the proposed methods have lower root-mean-square error and less noisy fringes in the filtering results than those of three existing “state-of-the-art” wavelet-domain phase-filtering methods. Tests using real InSAR data also demonstrate the superiority of the proposed methods in terms of visual and quantitative evaluation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".