DSMS GENERATION FROM COSMO-SKYMED, RADARSAT-2 AND TERRASAR-X IMAGERY ON BEAUPORT (CANADA) TEST SITE: EVALUATION AND COMPARISON OF DIFFERENT RADARGRAMMETRIC APPROACHES
Bibliographic record
Abstract
Abstract. This work is focused on the analysis of potentialities of the radargrammetric DSMs generation using high resolution SAR imagery acquired by three different platforms (COSMO-SkyMed, TerraSAR-X and Radarsat-2) with particular attention to geometric orientation models. Two orientation models have been tested in this work: the rigorous Toutin’s model, developed at the Canada Center for Remote Sensing (CCRS) and implemented in the commercial software package PCI Geomatica, and the radargrammetric model developed at University of Rome La Sapienza and implemented in the scientific software SISAR. A full comparison and analysis has been carried out over Beauport test site (Quebec, Canada), where a LIDAR ground truth and a dense set of GNSS CPs (Check points) are available. Moreover, a preliminary comparison between the DSMs extracted, respectively with SISAR and PCI-Geomatica has been performed. The accuracy of the generated DSMs has been evaluated through the scientific software DEMANAL developed by Prof. K. Jacobsen of University of Hannover. As regards orientation models, the results shown that the Toutin’s model accuracy is slightly better than the SISAR one, even if it is important to underline that the SISAR model is computed without using a priori ground truth information. As concern DSMs assessment, the global DSMs accuracy in term of RMSE is around 4 meter and the two radargrammetric approaches show similar performances.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".