Space resection in photogrammetry using collinearity condition without linearisation
Bibliographic record
Abstract
Space resection in photogrammetry involves determining the spatial position and orientation of a photo based on image measurements of ground control points that appear in the photo. Since space resection is a nonlinear problem, existing methods involve linearisation of the collinearity condition and the use of an iterative process to determine the final solution using the least-squares method. The process also requires initial approximate values of the unknown parameters, some of which must be estimated by another least-squares solution. This paper presents an optimisation model for space resection with or without redundancy that requires no linearisation, iterations, or initial approximate values. The model, which is nonlinear and noncovex, is solved using advanced Excel-based optimisation software that has been recently developed. Application of the model is illustrated using two numerical examples. The proposed model is simple and converges to the global optimal solution very quickly. The presented concept has many potential applications in solving nonlinear land surveying problems without the need for linearisation, and as such should be of interest to surveying and geomatics engineers.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".