Comparison between Range-Doppler and Rational-Function methods for SAR Terrain Geocoding
Bibliographic record
Abstract
Geocoding of Synthetic Aperture Radar (SAR) imagery is an essential preprocessing step in multi-source data integration, management and analysis for many geomatics applications. The Range-Doppler (RD) approach is a rigorous physical sensor model, which achieves the highest geometric accuracy and has been widely used in terrain-geocoding of satellite SAR data. However the main drawback is that it is not computationally efficient. The Rational-Function (RF) model has been successfully used for processing of optical images (push broom sensors IKONOS and QuickBird), and is of considerable interest in processing of SAR datasets as an alternative to the RD method because of its high computational efficiency and fitting accuracy (e.g. RADARSAT-2). Two SAR terrain-geocoding methods, based on the RD and the RF models, have been implemented at Canada Center for Mapping and Earth Observation (CCMEO). In this paper we briefly describe the well-known RD mathematical model and the RF mathematical model in more detail. The relative geometric accuracy for the two methods is analysed and compared. Finally, some major advantages of the RF method over the RD method are presented.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".