RADARSAT-1 Image Quality - Continuing Success in Extended Mission
Bibliographic record
Abstract
RADARSAT-1, the first Canadian SAR remote sensing satellite, was launched on November 4, 1995. After commissioning, it was put in to routine operations on April 1, 1996. Since then, it has been operating successfully, even after completing its five and a quarter years of design lifetime, and providing data to users for their intended applications. Significant effort continues to be expended in the provision of high quality products to users generated by the Canadian Data Processing Facility (CDPF). After initial calibration, both single beams and ScanSAR are monitored routinely as part of the Maintenance Phase for image quality performance. Image quality is monitored through periodic measurements of impulse response function, location error and radiometry, using images of the Amazon Rainforest and RADARSAT-1 Precision Transponders (RPTs). ScanSAR radiometry is also monitored through periodic measurements of the Amazon Rainforest. A major upgrade of the ScanSAR processor completed recently in CDPF made significant improvements in image quality and radiometry. Measured results indicate that image quality is better than system specification and maintained. This paper will describe the overall process of data acquisition, data analysis and recalibration for image quality maintenance. 1.
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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 | 0.005 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".