Ship detection using X-band dual-pol SAR data
Bibliographic record
Abstract
The interest for maritime surveillance and ship detection in particular has been growing during the last years. In this context, spaceborne SAR systems may contribute to the improvement of security and safety at sea. As such, to allow observation of non-cooperative boats, the revisit times of such systems must be compatible with the objectives of reactivity of maritime surveillance. Under CNES (French Space Agency) initiative, an airborne campaign using the ONERA Airborne SAR SETHI took place over the Mediterranean Sea. The main objective of this dedicated campaign of acquisition was to perform very precise measurements of sea clutter and ship Radar Cross Sections (RCS) for various conditions of acquisition (sea state, observation angle, boat type). This paper is focused on X-band dual-polarized SAR data acquired at 50° incidence angle and we show that such polarimetric configuration provides significant gain on ship detection compared to a mono-polarized SAR without degrading the revisit time like that would be the case with quad-pol SAR 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 | 0.001 | 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) | 0.001 | 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".