Ground surface displacement measurement from SAR imagery using deep learning
Bibliographic record
Abstract
Offset tracking using synthetic aperture radar (SAR) amplitude imagery is a valuable technique for detecting large ground displacements. However, the traditional offset tracking methods with the SAR datasets are computationally intensive and require significant time for processing. We have developed a novel cross-connection Siamese ResNet (CC-ResSiamNet). The model leverages multi-kernel offset tracking for preprocessing, followed by deep learning architectures that incorporate U-Net, cross-connections, and residual and attention blocks to predict pixel offsets between two SAR amplitude images. It is trained and tested on 200 K pairs of reference and secondary SAR amplitude images, alongside corresponding target offset data from Alaska's glaciers. The comparative analysis with multiple deep learning models confirmed that our designed model is highly generalizable, achieving rapid convergence, minimal overfitting, and high prediction accuracy. Through multi-scenario inference with glacier movements, earthquakes, and volcanic eruptions worldwide, the model demonstrates strong performance, closely matching the accuracy of traditional methods while offering significantly faster processing times through parallel computing. The model's rapid displacement mapping capability shows particular promise for improving disaster response and near real-time surface monitoring. While the approach encounters challenges in accurately capturing small-scale displacements, it opens new possibilities for SAR-based surface displacement prediction using machine learning. This research highlights the advantages of combining deep learning with SAR imagery for advancing geophysical analysis, with future applications anticipated as more commercial and scientific SAR missions launch globally. • A novel deep learning model is developed to predict pixel offset using radar images. • The model is trained on a large dataset from glaciers in Alaska and Yukon Territory. • The model is evaluated for its accuracy in predicting offsets. • The model has the potential to predict displacements from earthquakes and volcanoes.
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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".