Impact of DEM source on Radarsat-2 polarimetric information during ortho-rectification
Bibliographic record
Abstract
Ortho-rectification using digital terrain models is a key issue for full polarimetric complex synthetic aperture radar (SAR) data because resampling the complex data can corrupt the polarimetric phase, mainly in terrain with relief. Two methods for ortho-rectification of the complex SAR data can be applied: the polarimetric processing is performed before (image-space method) or after (ground-space method) the geometric processing. This research evaluated the impact of the digital elevation models (DEMs), which are generally available to users (topographic DEM and ASTER GDEM V2). The two methods were applied to three Radarsat-2 fine-quad data acquired with different look angles over a hilly relief study site. Quantitative evaluations between the two approaches as a function of different geometric and radiometric parameters were, thus, performed to evaluate the impact during the ortho-rectification. The results demonstrated that the look angles and the terrain slopes can potentially corrupt the single-look polarimetric complex SAR data during its ortho-rectification with the ground-space method, mainly at the layover limit. However, advice is provided to reduce these impacts to an acceptable level.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".