Adaptive sequential estimator for InSAR time series phase estimation
Bibliographic record
Abstract
The coherence estimation errors in phase linking can be mitigated through the weighted alignment of interferometric pairs and the intermediate filtering of data subsets. The Sequential Estimator (SE) serves as a representative method. It divides the coherence-weighted matrix into smaller subsets, using image compression and recursive estimation to enhance phase linking. However, the SE method has inherent limitations due to its dependence on fixed subset size and manual parameter setting, which hinder its application in complex, natural scenarios. In such environments, the distributions of coherent and low-coherence signals are often unpredictable. To address such limitations, this paper proposes an Adaptive Sequential Estimator (ASE) method. First, an adaptive coherence-weighted matrix partitioning method is proposed. Utilizing Otsu’s algorithm and a local subset merging algorithm, it adaptively generates data subsets which are dynamically tailored to the coherence distribution. Second, a modified sequential estimator is proposed. It selects the optimal subsets from the list with multiple merging degrees, to guide image compression and recursive phase estimation. Based on these, the ASE method adaptively prioritizes coherent information while minimizing the impact of decorrelation noise, thereby improving phase estimation accuracy. Experimental evaluation is conducted using 30 Radarsat-2 SAR images with VV polarization, including the quantitative and visual comparisons between the ASE method and existing methods. The results indicate that the ASE method outperforms other methods, and is particularly well-suited to handling the variable coherence matrix in natural scenarios. Compared to SE, the ASE method increases the distributed scatterer point density with 7%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".