3D PHYSICAL VERSUS EMPIRICAL MODELS FOR HR SENSOR ORIENTATION AND ELEVATION EXTRACTION: EXAMPLES WITH IKONOS AND QUICKBIRD
Bibliographic record
Abstract
Elevations for digital surface model (DSM) generation were extracted from different stereo highresolution (HR) images (QuickBird and Ikonos) using 3D physical and empirical geometric models. The 3D physical model is Toutin’s model (TM) developed at the Canada Centre for Remote Sensing, and the empirical model is the rational function model (RFM). First, Vendorsupplied RFMs refined with polynomial functions and TM were compared for the sensor orientations with leastsquares adjustments with different number of ground control points (GCPs). TM and RFMs gave similar results with Ikonos as soon as RFM was refined with a shift computed from at least one GCP. On the other hand, TM gave better results than RFMs with QuickBird regardless of the number of GCPs. Due to relief dependency, QuickBird RFM needed to be refined at least with linear functions computed from at least 610 GCPs. Some large errors were, however, noted on forward image RFM in column. The stereoextracted elevations of DSMs were then compared to 0.2m accurate Lidar elevation data. Because DSM stereoextracted elevations included the height of land covers (trees, houses), elevation linear errors with 68 percent confidence level (LE68) were computed for the entire area and three landcover classes (forested, urban/residential, bare surface). TM and RFMs with Ikonos, regardless of the method and GCP number, achieved comparable results for all classes while TM achieved overall better results than RFMs with QuickBird. All results demonstrated the necessity of refining Ikonos RFM with a tridirectional shift and at least one GCP but QuickBird RFM with 1 st order linear functions and 610 GCPs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".