Multiplicative and Product Model Constraints Upon Speckle Filtering of SAR Images
Bibliographic record
Abstract
Speckle filter performances depend strongly on the speckle and scene models used as the basis for filter development. These models that incorporate implicitly certain assumptions on speckle, scene and observed signals, were generally adopted and used without any justification. In this study, the multiplicative and the product speckle models, which have been used as the basis for the development of the most well known filters, are analyzed. Their implicits assumptions are discussed with regards to the stationarity-nonstationarity of speckle, observed and scene signals. Two categories of speckle filters are distinguished as a function of the stationarity-nonstationary assumption on speckle random variations. The various approximate models used for the multiplicative speckle noise model are then assessed as functions of speckle and scene characteristics. The Madsen method [6] was extended to the various models to derive the requirements on scene signal variations for the validity of the multiplicative stationary speckle model, and the product model which forces speckle to be a nonstationary process.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".