ABSTRACT Geometric Calibration and Validation of ASAR Imagery
Bibliographic record
Abstract
We describe work conducted to calibrate and then validate the geometry of ENVISAT ASAR products. A systematic error in range location was observed in ASAR products during the commissioning phase. A careful and complete analysis has been performed to establish the precise error. It has been compensated by updating the range gate bias (or sampling window start time bias). Validation of the absolute location accuracy of most ASAR products was performed subsequently. The location of surveyed targets is predicted using the satellite state vectors and ancillary timing information via the range and Doppler equations. The prediction's accuracy is affected by instrument bias, ionospheric and atmospheric path delay, as well as target survey errors. Transponders are in addition subject to internal delay uncertainty. The positions of the strong transponder and corner reflector targets in the images are measured to sub-sample accuracy by employing large oversampling factors. Initial and residual bias determinations were made using image acquisitions covering transponders and corner reflectors in the Netherlands, Canada, and Switzerland. Using a large number of independent targets helps reduce the influence of their independent survey errors. The highest resolution slant range single look complex (SLC) products (IMS, APS) were mainly used for testing. In addition, absolute location error was also measured on selected ground range products (IMP, APP, IMM, APM, WSM). Some ground range products also require treatment of multiple slant/ground range polynomials- proper handling is validated. For all test cases processed with precise orbits to date, the residual bias in the slant range direction has been smaller than the size of a single range sample. Predictability of target image location within ASAR image products is very high-better than experience with ERS-1/2, JERS-1, and RADARSAT-1. This result is encouraging, as it opens possibilities for ground control point (GCP) free terrain-geocoding and simplified interferometric processing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".