Subpixel image matching based on Fourier phase correlation for Radarsat-2 stereo-radargrammetry
Bibliographic record
Abstract
Image matching is the major step in the radargrammetric process to measure elevation parallax. To extract parallax from stereo synthetic aperture radar images the subpixel image matching method based on Fourier phase correlation was implemented with an algorithm using the hierarchical multiresolution approach and applied to Fine Quad mode Radarsat-2 data. The experimental results with simulated images show that a decrease in intersection angle leads to an increase in matching accuracy of up to 0.06 of a pixel. To validate the matching results a digital surface model was extracted from the real stereo pair and compared with accurate lidar data. The statistics show that there are good improvements (in the order of 10%–20%) in the accuracy over results extracted using a traditional image matching technique based on the normalized cross-correlation. The analysis of the mutual dependence of matching accuracy and stereo pair configurations shows that the application of subpixel matching allows us to make the ra...
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".