Off-Nadir Photogrammetry for Airborne SAR Motion Compensation: A First Step
Bibliographic record
Abstract
A photogrammetry system operated simultaneously with a synthetic aperture radar (SAR) from the same aerial platform provides strong sensor fusion possibilities that can improve the accuracy of repeat pass Interferometric SAR (InSAR) processing. Motion compensation is a key step in airborne SAR/InSAR processing, and the availability of accurate external digital elevation models (DEMs) is at the heart of many InSAR applications. Eventual research goals are (1) to produce high precision photogrammetric DEMs as reference for interferometric and tomographic applications, and (2) to use photogrammetric block adjustment parameters to fine-adjust the flight trajectory for enhanced motion compensation in repeat pass InSAR. To meet these goals; as SAR is oblique looking by design, an un-conventional off-nadir photogrammetric field-of-view coinciding with the SAR swath is required for our combined system. In this paper we focus on the accuracy implications of the derived photogrammetric DEMs from off-nadir vs nadir configuration as well as the potential for trajectory refinement from the derived photogrammetric block adjustment parameters. Additionally, we carry out an accuracy comparison of our photogrammetric system with established references (Fairbanks fodar™ and WorldDEM™). Vertical photogrammetric DEMs produced with our system over a test site near Silver City, Yukon Territory, Canada were found to be accurate with a mean height difference of 0.45 m and a corresponding standard deviation of 0.78 m from the reference fodar™ system; whereas produced nadir vs. off-nadir DEMs were found in accordance with each other with a mean difference of 0.29 m and standard deviation of 0.75 m. Furthermore, the estimated flight trajectory refinement for the test data had a mean value of 0.19 m with a standard deviation of 0.09 m.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".