An object-based region-growing phase unwrapping method for mapping vertical displacement in permafrost landscapes
Bibliographic record
Abstract
• The first customized unwrapping method is developed for permafrost landscapes. • An object-based adaptive method is proposed to handle regions with different residue densities. • A new framework is proposed to alleviate the impact of the decorrelation phase while in phase unwrapping. Terrain displacement due to the seasonal thaw of the active layer above permafrost can be sensitive to climate change; however, its accurate characterization remains a challenge. This study aimed to improve the measurement of the subsidence or vertical ground surface displacement using differential synthetic aperture radar interferometry (InSAR). Existing methods for reliable phase unwrapping are hindered by the decorrelation between time-series of SAR acquisitions that can result due to the heterogeneity and structural sensitivity of permafrost landscapes to external conditions. In this study, an advanced phase unwrapping method was proposed, in which three types of regions, namely non-residue, sparse residue, and dense residue objects, were obtained from wrapped interferogram and residue map using a segmentation method. Two variants of Polynomial-Based Region Growing Phase Unwrapping (PBRGPU) were developed, which are sparse-residue Object-based PBRGPU(SOP) and dense-residue Object-based PBRGPU(DOP). The results demonstrated that the proposed method outperformed the existing phase unwrapping methods by partially suppressing the decorrelation phase and enhancing robustness for complex terrain deformation in the absence of measured field data. Both the PBRGPU variants and segmentation strategies compose the object-based unwrapping method for permafrost, and also provide a new framework by combining the segmentations and scenarios for phase unwrapping for permafrost regions.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".