Comparative analysis of SAR sensors for effective tectonic lineament mapping in semiarid region
Bibliographic record
Abstract
The progression and refinement of remote sensing techniques now allow the extraction of geological lineaments without traditional methods. This study aims to detect lineaments using Synthetic Aperture Radar (SAR) data from three sensors with different bands: Alos-Palsar, Radarsat-1, and Sentinel-1. Automatic lineament extraction is performed by combining two different parameters along with the Palsar Digital Elevation Model in order to recommend the most powerful sensor for this task. The methodology involves relating the length, number, orientation, and density of lineaments to surface features such as slope, lithology, and discontinuities. The results of this evaluation show that the lineaments obtained of both polarizations of sentinel correlate better with geological units, the orientation of the tectonic system, shadow and slope maps. This is attributable to the high efficiency of VH polarization, which is not dependent on soil characteristics, in comparison with other polarizations that overestimated lineaments with different directions.
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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 | 0.002 | 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".