Iceberg Detection Using Simulated Dual-Polarized Radarsat Constellation Data
Bibliographic record
Abstract
Iceberg monitoring is an important application of synthetic aperture radar (SAR), and is one of the stated objectives of the Radarsat Constellation, the next generation of Canada’s Radarsat satellites. In this paper, we simulate Radarsat Constellation data in a number of different imaging modes, using Radarsat-2 single-look complex data covering a study area in the Labrador Sea. We test the iceberg detection performance of both linear dual-pol data as well as compact polarimetry, a novel SAR architecture that transmits circular polarization rather than the traditional horizontal or vertical polarizations. We use the likelihood ratio test method to calculate a decision variable image for each of a number of different dual-pol and compact configurations, then analyze the detection performance using 25 validated iceberg locations spread across 12 different scenes. We found that compared to the linear data, the compact data missed fewer targets, and detected a greater number of pixels of detected targets, for most of the incidence angles and imaging modes tested. Compact polarimetry seems to be a promising choice for iceberg detection applications.
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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 | 0.001 | 0.001 |
| 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 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".