A comparison of radar altimetry and repeat pass interferometry as methods of producing digital terrain elevation models
Bibliographic record
Abstract
Two methods used to produce digital terrain elevation models (DTEMs) are considered: radar altimetry and repeat-pass interferometry. In the case of the radar altimetry-derived DTEM, elevation values are obtained by subtracting aircraft altimeter measurements from GPS measurements of absolute aircraft height. Discrete elevation values are interpolated to form a continuous surface. Repeat-pass interferometry with RADARSAT-1 data is used to produce the second DTEM, which has then been georeferenced by means of ground control points. The two models are compared in terms of positional accuracy of terrain features and resolution of small-scale landforms. The interferometry-derived model is of higher resolution than the altimetry-derived model; this is expected based on the method of data collection. Positional discrepancies between terrain features are also identified. These discrepancies are attributed to the incidence angle of the RADARSAT-1 satellite relative to feature orientation. Georeferencing using control points is essential to the success of producing terrain models using repeat-pass interferometry.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".