Sensitivity Testing of Stereophotoclinometry for the OSIRIS-REx Mission. I. The Accuracy and Errors of Digital Terrain Modeling
Bibliographic record
Abstract
Abstract Stereophotoclinometry (SPC) was the prime method of shape modeling for NASA’s OSIRIS-REx mission to asteroid Bennu. Here we describe the extensive testing conducted before launch to certify SPC as NASA Class B flight software, which not only validated SPC for operational use but also quantified the accuracy of this technique. We used a computer-generated digital terrain model (DTM) of a synthetic asteroid as the truth input to render simulated truth images per the planned OSIRIS-REx observing campaign. The truth images were then used as input to SPC to create testing DTMs. Imaging sets, observational parameters, and processing techniques were varied to evaluate their effects on SPC's performance and their relative importance for the quality of the resulting DTMs. We show that the errors in accuracy for SPC models are of the order of the source images’ smallest pixel sizes and that a DTM can be created at any scale, provided there is sufficient imagery at that scale. Uncertainty in the spacecraft’s flight path has minimal impact on the accuracy of SPC models. Subtraction between two DTMs (truth and simulated) is an effective approach for measuring error but has limitations. Comparing the simulated truth images with images rendered from the SPC-derived DTMs provides an excellent metric for DTM quality at smaller scales and can also be applied in flight by using real images of the target. SPC has limitations near steep slopes (e.g., the sides of boulders), leading to height errors of more than 30%. This assessment of the accuracy and sensitivity of SPC provides confidence in this technique and lessons that can be applied to future missions.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".