Reducing patch-like Errors in SAR offset tracking displacements using logarithmic transformation and a weighted NCC algorithm
Bibliographic record
Abstract
Pixel offset tracking (OT) algorithm is a useful tool for measuring large surface displacements by matching amplitudes in master and slave synthetic aperture radar (SAR) images. However, strong backscatters can cause homogeneous errors within a matching window (referred to as patch-like errors) in traditional OT processing, thereby misleading the interpretation of displacement events, especially over a small area. In this letter, we proposed an improved SAR OT algorithm to reduce patch-like errors. In which, a logarithmic transformation was firstly utilized to narrow the SAR amplitude range between strong and weak back scatterers. Strong backscatters causing patch-like errors were then statistically detected with an indicator of median absolute deviation. Finally, those strong backscatters were excluded from SAR OT processing using a weighted normalized cross-correlation scheme, in order to reduce the caused patch-like errors. Two real data tests over the Shuozhou and Yulin coal mining areas, China, suggest that the mean accuracy of the displacements estimated by the presented method improved about 30%, with respect to that estimated by the traditional OT algorithm. The proposed SAR OT algorithm offers a robust option to measure large displacements, especially over a small area, associated with anthropologic or geophysical activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".