Data Quality Requirements for Future SAR
Bibliographic record
Abstract
SAR users have varying data quality requirements. It is their demands, market share, and strategic importance that ultimately dictate many fundamental engineering decisions for the design of the sensor and supporting ground segment. In this paper, we outline some considerations for the overall data quality requirements for future SAR systems, assuming that these missions will include imaging capabilities for applications with polarimetric, interferometric, and more traditional SAR modes that include single channels and/or single and multiple beams. Specifically, we draw from our experience with a number of existing SARs including ERS-1, ERS-2, J-ERS-1, and RADARSAT-1 to explore basic data quality issues that affect the geometric, radiometric, and interferometric (or phase) fidelity of products, and ultimately the reliability of the information products generated from them. These observations have implications on the design of satellite and ground segment components for future SARs such as ENVISAT and RADARSAT-2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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; both teacher heads agree on what is shown here.
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".