A Study on the Effect of Speckle Noise in Modeling Sea Clutter and a Mellin Transform-Based Method for Weibull Parameter Estimation
Bibliographic record
Abstract
Target detection in sea clutter for satellite-based images is studied. The detection process is based on the Constant False Alarm Rate (CFAR) algorithm. To obtain the adaptive threshold, we conduct a thorough spatial statistical analysis of the sea clutter. The common issue with satellite-based Synthetic Aperture Radar (SAR) images is the contamination with speckle noise. The goal of the paper is to study the effect of the speckle noise on the statistical properties of the sea clutter. Based on the experimental data gathered from the Canadian RADARSAT-1 satellite, we demonstrate that the Weibull, Rayleigh, and K distributions are capable of modeling the statistical properties of the sea clutter in the presence of the speckle noise more precisely while Weibull, Gamma, inverse Gaussian, and Log-normal distributions describe the statistical properties of the sea clutter with higher accuracy when the speckle noise is removed. The goodness-of-fit measure is based on the Kullback-Leibler (KL) divergence metric. The speckle noise removal process is based on median filtering with the Peak Signal to Noise Ratio (PSNR) of the image as a measure for the filter parameter estimation.The presented results, indicate that the Weibull distribution is able to model the statistical properties of the sea clutter both in the presence and absence of the speckle noise with high accuracy. To estimate the parameters of the Weibull distribution, we propose a method based on the Mellin transform which compared to the existing techniques, provides a closed-form and untangled solutions for both parameters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".