An all-optronic synthetic aperture lidar
Bibliographic record
Abstract
Synthetic Aperture Radar (SAR) is a mature technology that overcomes the diffraction limit of an imaging system’s real aperture by taking advantage of the platform motion to coherently sample multiple sections of an aperture much larger than the physical one. Synthetic Aperture Lidar (SAL) is the extension of SAR to much shorter wavelengths (1.5 μm vs 5 cm). This new technology can offer higher resolution images in day or night time as well as in certain adverse conditions. It could be a powerful tool for Earth monitoring (ship detection, frontier surveillance, ocean monitoring) from aircraft, unattended aerial vehicle (UAV) or spatial platforms. A continuous flow of high-resolution images covering large areas would however produce a large amount of data involving a high cost in term of post-processing computational time. This paper presents a laboratory demonstration of a SAL system complete with image reconstruction based on optronic processing. This differs from the more traditional digital approach by its real-time processing capability. The SAL system is discussed and images obtained from a non-metallic diffuse target at ranges up to 3m are shown, these images being processed by a real-time optronic SAR processor origiinally designed to reconstruct SAR images from ENVISAT/ASAR data.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".