Stereo-mate generation of high-resolution satellite imagery using a parallel projection model
Bibliographic record
Abstract
Synthesis methods to create a stereo-mate of satellite imagery from an orthophoto have been developed in many previous studies. If these methods are applied in an urban area where there are many adjacent tall buildings, stereo viewing is inhibited by occlusion in the orthophoto and its stereo-mate. In high-resolution satellite imagery, the in-track view angle of the image is usually far from vertical; consequently, the occluded area near tall structures occupies a large area, and this severely affects stereo viewing. This study proposes a different approach to creating stereo-mates for high-resolution satellite imagery by projection of the digital surface model (DSM) draped by the original single image onto a fictitious satellite sensor model. The main benefit of this method is enhanced stereo viewing by arranging the fictitious sensor model to reduce occlusion area. The physical sensor model of the original image is previously derived by parallel projection model, and then the stereo-mate fictitious sensor model is determined from the physical sensor model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".