A new method for speckle reduction in Synthetic Aperture Radar (SAR) images using optimal window size
Bibliographic record
Abstract
Speckle degrades the radiometric quality of a Synthetic Aperture Radar (SAR) image and makes its visual interpretation difficult. The approaches proposed previously for speckle filtering of SAR images exploit a window of fixed size for this purpose. But a fixed size window is not sufficient as the size of objects may vary throughout the image. In this paper, a method is introduced by which each pixel in the image is filtered using a window size which is optimal for that pixel. Real and imaginary parts of a single-channel SAR image are used for the selection of the best window size for each pixel, and then intensity image is filtered by applying that window size. The Average and Minimum Mean Square Error (MMSE) filters are modified using the Adaptive Window Size method. This approach is implemented on the HH-channel of a RADARSAT-2 image acquired over the Avalon Peninsula near St. John's, Newfoundland, Canada. This filter can supress speckle effectively while retaining the details reasonably.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".