Stability analysis of low‐cost digital cameras for aerial mapping using different georeferencing techniques
Bibliographic record
Abstract
Abstract Increasing resolution and lower cost of off‐the‐shelf digital cameras are giving rise to their use in traditional and new photogrammetric activities such as aerial mapping, transportation and surveillance as well as archaeological, industrial and medical applications. For most, if not all, photogrammetric applications, the interior orientation parameters (IOP) of the camera need to be determined and analysed. The derivation of these parameters is usually achieved through a bundle adjustment with self‐calibration procedure. Prior to using a camera in photogrammetric applications, the IOP should be estimated and their stability should be checked. Camera stability has been rarely addressed when dealing with analogue metric cameras since they have been carefully designed and built to assure the utmost stability of their internal characteristics. However, the stability of low‐cost digital cameras needs to be investigated since these cameras are not built with photogrammetric applications in mind. This paper introduces three quantitative methods for testing camera stability, where the degree of similarity between reconstructed bundles from two sets of IOP is evaluated. Each of these methods limits the position and orientation of the bundles in a different way. Hence, each method is applicable for a specific georeferencing methodology depending on similar constraints imposed by the stability measures and different georeferencing techniques. The paper will test this hypothesis on the basis of reconstruction results obtained from the use of a low‐cost digital camera in an aerial mapping project.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".