Comparison of stereo-extracted DTM from different high-resolution sensors: SPOT-5, EROS-A, IKONOS-II, and QuickBird
Bibliographic record
Abstract
Digital elevation models (DEMs) extracted from high-resolution stereo-images (SPOT-5, EROS-A, IKONOS-II, and QuickBird) using a three-dimensional multisensor physical model developed at the Canada Centre for Remote Sensing, Natural Resources Canada were evaluated. In a first step, the photogrammetric bundle adjustment was setup for the stereo-images with few accurate ground control points. In a second step, DEMs were generated using an area-based multiscale image matching method and then compared to 0.2-m accurate light detection and ranging (LIDAR) elevation data. Elevation linear errors with 68% confidence level (LE68) of 6.5, 20, 6.4, and 6.7 m were achieved for SPOT, 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. However, when such differences were not large, LE68 of 10 m (four pixels) was achieved. 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 no difference between the stereo-extracted elevations and the LIDAR data. LE68 of 2.2, 1.5, and 1.2 m were then obtained for SPOT, IKONOS, and QuickBird, respectively. When compared to sensor resolution, multidate across-track SPOT with a smaller base-to-height (B/H) ratio of 0.77 achieved three to four times better results than same-date in-track IKONOS and QuickBird with B/H of around 1: 0.5 pixels versus 1.5 or 2 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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".