Extraction of Peak Feature Based on Synthetic Sinc Model in SAR images
Bibliographic record
Abstract
Abstract. Peak is an important feature in Synthetic Aperture Radar(SAR), which represents essence of scattering centre. There are two general approaches in the literature to extract peak. One way is to extract peak after speckle suppression filtering. Using this method, the extracted feature is in accurate, and the algorithm is more complicated. Another is that detecting the amplitude of the peak directly. In order to have a fast and accurate peak extraction, we proposed using the Sinc peak model algorithm in this paper. It directly extracts peak features from the original SAR image without any noise suppression filtering, which is instead of Gauss mask function. The estimation parameters of peaks use QE theorists. Finally, we can get accuracy three parameters to describe peak features. Analytic fluctuation of parameters is compared with Gauss model peaks using truth SAR images. Experimental demonstrate that the new algorithm is more effective than others for extracting peak features in SAR images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".