On the use of Compact Polarimetry SAR features for the monitoring of a crashed aircraft in the Western part of King George Island, Antarctica
Bibliographic record
Abstract
Synthetic Aperture Radar (SAR) is an active, all-day and all-weather, high-resolution microwave sensor able to measure the electromagnetic field backscattered off the observed scene. Antarctic environment presents very hard imaging conditions for optical imagery, and sometimes they are challenging even for SAR observations. However, full-polarimetric (FP) SAR features can provide useful information for characterizing man-made targets in different types of ice. This study aims at exploiting polarimetric features extracted from compact-polarimetric (CP) SAR architectures, e. g., circular transmitting/linear receiving (CTLR) and linear transmitting/linear receiving (LTLR), that have been shown to be operationally attractive due to the doubled area coverage they offer with respect to FP SAR architectures. CP SAR data are here emulated using actual FP SAR measurements to both detect the dominant scattering mechanism that characterizes man-made targets, and classify them accordingly. As a study case, an aircraft crashed on November 2014 off the Chilean military base area in the western of King George Island (Antarctica) and actually placed close to the gravel airstrip, is considered. At all, three Radarsat-2 FPSAR data were acquired over the test site. In this study, as first results, a single SAR acquisition was explored due to the availability of ground truth relevant to the crashed aircraft and other airdrome structures position. The achieved preliminary results have encouraged future work that will deal with the exploration of different responses from a wide range of inland and sea ice.
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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 | 0.001 |
| 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 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".