GENERATION AND EVALUATION OF RADARGRAMMETRIC DEM FROM RADARSAT-1 STANDARD IMAGES IN LOW RELIEF AREA IN THE AMAZON COASTAL PLAIN
Bibliographic record
Abstract
ABSTRACT. The generation of digital elevation models (DEMs) from the Standard imaging mode of RADARSAT-1 stereo-images was investigated to evaluate theviability of producing 1:100,000 scale altimetric maps in areas with a low topographic relief on the Brazilian Amazon coastal plain. Absolute DEMs were generatedusing RADARSAT-1 Standard stereopairs (S2Asc/S1Des, S6Des/S1Des, and S7Asc/S6Des) with ground control points collected using a Differential Global Positioningsystem. The geometric modeling for the DEM extractions was based on the “RADARSAT Specific Model” from the OrthoEngine Satellite Edition of the PCI Geomaticasoftware; this model is an automated matching solution that considers the slant range distances from sensors and terrain. Thirteen independent control points were usedto validate the accuracy of the absolute DEM. Only the S2Asc/S1Des pair was effective in highlighting depth information, which was a result of the pair’s intermediateintersection angle (47◦) and higher vertical parallax ratio (4.31). Therefore, RADARSAT-1 Standard images are a useful alternative for generating absolute DEM at thescale of 1:100,000 in cartographic gap areas on the Amazon coastal plain.Keywords: digital elevation model, stereoscopy, RADARSAT-1, Amazon, Brazil. RESUMO. A geração de modelos digitais de elevação (MDEs) a partir de pares estereoscópicos RADARSAT-1 modo Standard foi empregada com o objetivo deavaliar a produção de mapa altimétrico na escala de 1:100.000 em uma área de baixo relevo na planície costeira amazônica. MDEs absolutos foram gerados usandopares estereoscópicos RADARSAT-1 Standard (S2Asc/S1Des, S6Des/S1Des e S7Asc/S6Des) com pontos de controle do terreno coletados usando-se um sistema deposicionamento global diferencial. Omodelamento geométrico para extração doMDE foi baseado no “Modelo Específico para o RADARSAT”, do programa PCIGeomatica, através do cálculo que maximiza o coeficiente de correlação e leva em consideração as distâncias no alcance inclinado entre o sensor e o terreno. Para a validação do MDE absoluto foram usados 13 pontos de controle independentes. Apenas o par S2Asc/S1Des foi eficaz no realce da informação de profundidade, devido aos ângulos de intersecção intermediários (47◦), mas principalmente, devido a maior razão da paralaxe vertical observada (4,31). Portanto, as imagens RADARSAT-1 Standard representam uma ótima alternativa para a produção de MDEs absolutos na escala de 1:100.000 em áreas com vazios cartográficos na planície costeira amazônica.Palavras-chave: modelo digital de elevação, estereoscopia, RADARSAT-1,Amazônia, Brasil.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".