A Transformer-Based Architecture for InSAR Phase Unwrapping Under Noisy Conditions
Bibliographic record
Abstract
Phase unwrapping is a crucial step in deriving deformation from interferometric synthetic aperture radar data, yet challenges such as discontinuous noise in low-coherence areas and random sensor noise can significantly compromise reconstruction accuracy and efficiency. Existing methods often struggle under noisy conditions and exhibit slower processing times. To overcome these limitations, this paper introduces Phaseformer, a transformer-based model designed to predict the wrap count at each pixel directly from wrapped phase maps. Due to the limited size of the real-world dataset, a simulated dataset with diverse noise levels was developed to train the model. Phaseformer achieved the lowest RMSE of 1.88 rad, a success ratio of 95.6%, and a runtime of 0.01 s per image, outperforming both state-of-the-art methods and traditional methods. For real-world scenarios, unwrapped phase results were integrated into a post-processing workflow using MintPy to derive time-series deformation. Interferograms from Hawaii and Mexico City, generated using Sentinel-1 data and validated with GPS ground truth, revealed consistent deformation rates with minimal loop closure errors. These results highlight Phaseformer’s potential for accurate and efficient phase unwrapping in complex, real-world applications.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".