Bundle Adjustment With Rational Polynomial Camera Models Based on Generic Method
Bibliographic record
Abstract
A rational polynomial camera (RPC) model is a kind of generic sensor model that can be used in different remote sensing systems to model the relationship between object space and image space and transform image data to conform to a map projection. Unlike traditional physical camera models, an RPC model has many coefficients (a total of 80), and these coefficients do not have a physical interpretation. This represents a difficult challenge for the mapping community. For RPC refinement, many solutions, including direct and indirect methods, have been developed. One of them, the recent developed generic method has been shown to be a robust method. Because the generic method can simulate the camera's exterior parameters, it can be used in any geometric situation. Even so, the performance of bundle adjustment with the generic method is still unknown. In this paper, through experiments with a stereo pair and a stereo triplet, the capability of high-accuracy geopositioning based on the generic method is demonstrated. We first give a brief review of previous bundle adjustment methods based on RPC. Then, the bundle adjustment algorithm based on the generic method is introduced in detail. We finally present the experiments with both IKONOS and QuickBird imageries. The experiments show that the bundle adjustment based on the generic method can reach subpixel accuracy in image space and submeter accuracy in object space.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".