Satellite Data Fusion Techniques for Terrain and Surficial Geological Mapping
Bibliographic record
Abstract
Increased satellite image data availability, with different spatial, spectral, and radiometric resolutions, present some challenges for image fusion techniques to be used in many application areas. In this study, we demonstrate an improved technique combining RADARSAT (SAR), Digital Elevation Model (DEM) and Landsat (ETM+) images with surficial geology data into two complementary composite image maps within Mackenzie Valley Pipeline Corridor, Canada. The method includes four processing steps. (1) The shaded relief derived from DEM was blended with SAR data to produce 3-D effect for terrain and geological structural interpretation. (2) Image fusion using a pan-sharpened IHS technique produced a higher resolution multi-spectral (MS) image. (3) The higher resolution MS image is blended with the SAR-DEM to produce an image terrain map. (4) Two complementary composites were then produced. The first image map contains of geomorphic features/surficial geology GIS layers, and 3D terrain information derived from the SAR-DEM image. The second image map is a composite of geomorphic features/surficial geology blended with the SAR-DEM-ETM+ image map. Integrated image maps generated in steps 1 and 3 provide standardized image base maps to display surficial geological and terrain information.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".