Joint Spatio-Temporal Correction for Both Turbulent and Stratified Tropospheric Delays in TS-InSAR
Bibliographic record
Abstract
Synthetic aperture radar interferometry (InSAR) has been applied in ground deformation monitoring. However, the interferograms are susceptible to tropospheric delay artifacts, including both turbulent delays and stratified ones, especially in mountainous regions, due to temporal and spatial variation of temperature, humidity and pressure in the troposphere. Most atmospheric correction methods only address one of the delays and lack comprehensive consideration, thus it is hard to suppress the atmospheric artifacts completely. In this paper, we proposed a joint spatio-temporal correction (JSTC) method for both turbulent and stratified delays, which applies a dual-scale temporal low-pass filter to separate turbulent delays from other low-frequency components in temporal domain, then adopts the quadtree segmentation to correct stratified delays caused by spatial heterogeneity. The proposed method was first validated by simulated data, and then tested by real data in the mountainous region. The results demonstrated that our method can significantly reduce the impact of atmospheric artifacts on the interferograms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".