A Comparitive Study On The Perfermance Of The InSAR Phase Filtering Approches In The Spatial And The Wavelet Domains
Bibliographic record
Abstract
Abstract. In this paper, we studied and tested different filtering approaches of the SAR interferograms in the spatial and wavelet domains. In the spatial domain, we applied the classic Lee filter and the Weighted Median Filter WMF. In the wavelet domain, we tested a noise reduction algorithm WInP proposed by López and Fàbregàs and its enhanced version FAMM developed by Abdelfattah and Bouzid. Those filters are validated with different SAR interferograms provided by Radarsat-2, Envisat, ERS-2 and COSMO-SkyMed SLC data acquired over regions of Mahdia and Ben Guerden in Tunisia. The aim of this study is to select the optimal filtering approach with respect to the fringe pattern in the interferogram. This selection is based on the Digital Elevation Model error computed between the filtered unwrapping phase image and the Global ASTER DEM of the same regions and verified with simulated interferograms.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".