SAR sidelobe apodization using the Kaiser window
Why this work is in the frame
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Bibliographic record
Abstract
Traditional synthetic aperture radar (SAR) image processing involves a two-dimensional Fourier transform that may produce significant high intensity sidelobes. These artifacts have the potential of obscuring low intensity scatterers in the image. Spatially variant sidelobe apodization is a technique that reduces sidelobe levels in a Fourier image while maintaining the image resolution that would be obtained using the rectangular window. In this paper, the use of the Kaiser (1974) window for sidelobe apodization is studied. It is shown that the use of this window outperforms the use of the cosine-on-pedestal window. Albeit, the method proposed here is not easily implemented, high resolution and low sidelobe levels are obtained. The method was tested and results are shown when using this new sidelobe apodization technique on an actual SAR image.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it