Simulating SCN and MSSR modes of RADARSAT-2 for ship and iceberg discrimination
Bibliographic record
Abstract
A tool developed for simulating RADARSAT-2 (RS2) Maritime Satellite Surveillance Radar (MSSR) mode data from higher resolution data is described. RS2 Fine and Fine Quad images containing validated ship and iceberg targets were resampled to low resolution ScanSAR Narrow (SCN) and MSSR mode data. This tool can be adopted to use other image modes as inputs and simulate other outputs as well and the simulated products are used to develop a ship and iceberg discriminator for those modes. A series of tests were applied to verify the accuracy of the backscatter characteristics of the simulated products and the performance of the target discriminator are presented for SCN and MSSR mode Ocean Surveillance, Very wide swath, Near incidence (OSVN)[1]. Since there was a very limited ship data suitable for simulating MSSR mode available, only a demonstration of MSSR OSVN classifier was included.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".