Research on InSAR Coherence Proxy and Optimization Method for Interferometric Network Construction in the Era of InSAR Big Data
Bibliographic record
Abstract
With the explosive growth of interferometric synthetic aperture radar (InSAR) data, we have entered the era of InSAR big data. A key challenge in big data InSAR processing is efficient construction of an optimal interferometric network from the vast number of available interferometric pairs. The conventional small spatiotemporal baseline set method aims to enhance the overall coherence of the interferometric network by constraining temporal and spatial baselines; however, it still presents several shortcomings in practice. Coherence-based interferometric network optimization methods have emerged to improve deformation monitoring accuracy and spatial coverage density. Nevertheless, calculating coherence for all possible interferometric pairs is computationally inefficient and hinders the implementation of near-real-time InSAR processing. To address this issue, we analyzed long-term coherence variations with respect to temporal baselines, spatial baselines, and seasonal changes, and developed a coherence proxy model to estimate a priori coherence for interferometric pairs. Leveraging this model, we proposed an interferometric network optimization method that integrates graph theory algorithms, specifically the minimum spanning tree (MST) and phase triangular completion, to ensure robustness and temporal consistency. The coherence proxy model was validated using both synthetic data and real data from five representative regions across China with diverse land cover types, and the proposed interferometric network construction method was likewise evaluated on both synthetic and real datasets. The results demonstrate that the coherence proxy model performs well on both synthetic and real data, requiring only approximately 300-400 interferograms to reliably model the proxy and estimate the prior coherence of all potential interferometric pairs. The proposed network construction method yields a highly coherent and structurally robust interferometric network, enabling high-accuracy and high-coverage deformation monitoring. For Sentinel-1 data spanning three years, it reduces the coherence computation workload by up to 90%. In the case of incremental network construction with newly acquired data, the method can completely eliminate the heavy computational burden of coherence estimation, thereby supporting the advancement of near-real-time InSAR processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".