Error Tracking in IKONOS Geometric Processing Using a 3D Parametric Modelling
Bibliographic record
Abstract
Thirteen panchromatic (Pan) and multiband (XS) IKONOS Geo-product images over seven study sites with various environments and terrain were tested using different cartographic data and accuracies with a 3D parametric model developed at the Canada Centre for Remote Sensing, Natural Resources Canada. The objectives of this study were to define the relationship between the final accuracy and the number and accuracy of input data, to track error propagation during the full geometric correction process (bundle adjustment and ortho-rectification), and to advise on the applicability of the model in operational environments. When ground control points (GCPs) have an accuracy poorer than 3 m, 20 GCPs over the entire image is a good compromise to obtain a 3- to 4-m accuracy in the bundle adjustment. When GCP accuracy is better than 1 m, 10 GCPs are enough to decrease the bundle adjustment error of either panchromatic or multiband images to 2-3 m. Because GCP residuals reflect the input data errors (map and/or plotting) these errors did not propagate through the 3D parametric model, and the internal accuracy of the geometric model is thus better (around a pixel or less). Quantitative and qualitative evaluations of ortho images were thus performed with either independent check points or overlaid digital vector files. Generally, the measured errors confirmed the predicted errors or even were slightly better, and 2-4 m positioning accuracy was achieved for the ortho images depending upon the elevation accuracy (DEM and grid spacing). To achieve a better final positioning accuracy, such as 1 m, a 1-2 m accurate DEM with fine grid spacing is required in addition to well-defined GCPs with an accuracy of 1 m.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".