A Deep Convolutional Neural Network With Multiscale Feature Dynamic Fusion for InSAR Phase Filtering
Bibliographic record
Abstract
Interferometric phase filtering is a crucial step in the interferometric synthetic aperture radar (InSAR) data processing, which is also important for improving the accuracy of topography mapping and deformation monitoring. Most of the commonly used phase filtering methods perform windowing computations based on the statistical characteristics of a single interferogram in the spatial or frequency domain. However, the difficulty in taking into account the diversity and complexity of the phase image results in filtering methods with weak denoising, limited detail preservation, and poor generalization ability. At the same time, regardless of the spatial or frequency domain, improved phase filtering performance inevitably leads to the problem of declining effectiveness. This paper proposes a phase filtering method (MSFF-DCNN) based on the deep convolution neural network (DCNN) with Multi-scale feature dynamic fusion. Unlike the traditional feedforward neural networks (FNN), the proposed method adopts a strategy of multi-scale feature dynamic fusion that accounts for the deep and shallow features of the interferometric phase while also taking into account image detail preservation and noise suppression during phase filtering. Based on both subjective and objective evaluations, the experimental results using the simulated data prove that the proposed method has better noise suppression and detail preservation than the commonly used methods and that the filtering performance is less dependent on noise level. Experiments using the real data confirm that the proposed method has better generalization ability and can meet the precision requirements of practical applications. The method presented in this paper can provide a new approach for research in high-precision InSAR data processing technology while also offering technical support for practical InSAR applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".