Analysis of SAR visibility and sensitivity based on Sentinel-1A images - a case study of Fengjie, the Three Gorges Region of China
Bibliographic record
Abstract
The Three Gorges Region is one of the regions with the most geological disasters in China. Fengjie county, located in the Chongqing section of the Three Gorges Region, is one of the most complicated areas of geological hazards in northeast of Chongqing. Synthetic Aperture Radar Interferometry (InSAR) technology has been proven to be an effective method for surface deformation monitoring, such as the earthquake, landslide and land subsidence etc. Nevertheless, the application of InSAR technology in landslide monitoring is limited by observation blind areas. These blind areas are mainly caused by the large scale of slope in the study area and the direction of satellite. And, these blind areas are also caused by the land cover changes. In this paper, we obtained the observation capability of Sentinel-1A ascending data in Fengjie county, and projection of true surface deformation in the line of sight direction by calculating the relationship of synthetic aperture radar data and digital elevation model data. And we analyzed the relationship about visibility, sensitivity and InSAR monitoring points. It provides a priori assessment of SAR image selection for geological disaster monitoring in the Three Gorges Region, and a regional interpretation method of InSAR deformation values for mountainous areas with significant terrain fluctuation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".