GENERATION OF DTM FROM STEREO HIGH RESOLUTION SENSORS
Bibliographic record
Abstract
Digital terrain models (DTMs) were extracted from high-resolution stereo images (SPOT-5, EROS-A, IKONOS-II and QuickBird) using a three-dimensional universal physical model developed at the Canada Centre for Remote Sensing, Natural Resources Canada. DEMs were generated using an area-based multi-scale image matching method and then compared to 0.2-m accurate lidar elevation data. Elevation linear errors with 68 % confidence level (LE68) of 5.5 m, 6.5 m, 20 m, 6.4 m and 6.7 m were achieved for SPOT-HRS, SPOT-HRG (5 m), EROS, IKONOS and QuickBird, respectively. The poor results for EROS are mainly due to its asynchronous low orbit, which generated large geometric and radiometric differences. The best relative results are obtained with SPOT5. Since the SPOT, IKONOS and QuickBird DEMs were in fact digital surface models, where the height of land covers was included, elevation accuracy was performed only on bare surfaces (soils and lakes), where there was also no difference between the stereo-extracted elevations and thelidardata. LE68 of 2.7 m, 2.2 m, 1.5 m and 1.2 m were then obtained for SPOT-HRS, SPOT-HRG (5 m), IKONOS and QuickBird, respectively. Relatively sensor resolution, multi-date across-track SPOT, with also a smaller B/H of 0.77, achieved three to four times better results than same-date in-track IKONOS and QuickBird with B/H of one: half-pixel versus 1.5 or two pixels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".