pyunwrap-insar 0.1.1

⚠️ Important Note

This project is actively under development. While the core functionality is production-ready and thoroughly tested, some advanced features are still being refined.

Physics-informed deep learning for InSAR phase unwrapping.

Why this exists · How it works · Install · Quickstart · Notebooks · Architecture · Citation

pyunwrap-insar unwraps Interferometric Synthetic Aperture Radar (InSAR) phase — the core measurement behind satellite-based ground-deformation monitoring — using a physics-informed U-Net that predicts an integer ambiguity map, not the unwrapped phase itself. The rewrapping identity is enforced by construction, so the model is structurally incapable of producing an output that contradicts the observed wrapped phase; it can only be wrong about how many 2π cycles were missed, never about the physics of wrapping.

Why this exists

Every classical phase-unwrapping algorithm — branch-cut methods, minimum-cost flow (the approach behind SNAPHU, the field's long-standing reference tool), Goldstein's algorithm — rests on one assumption: that the true phase gradient between adjacent pixels never exceeds π radians (the Nyquist/Itoh condition). Two situations break that assumption in practice:

• Low coherence. Vegetation, water, and temporal decorrelation add near-random phase noise. Once the local gradient becomes unreliable, branch-cut and MCF methods don't just get that one pixel wrong — they propagate the error across the connected region downstream of it.
• Steep deformation gradients. Earthquakes, volcanic inflation, and mining subsidence can produce genuine phase gradients that exceed π radians per pixel near the source. No amount of algorithmic cleverness recovers this from the wrapped phase alone without an external prior — which is exactly the gap a learned model can fill.

pyunwrap targets both regimes directly, and does so without inheriting the most common failure mode of naive deep-learning approaches to this problem: regressing the unwrapped phase directly gives a network no reason to respect wrap(prediction) == observed_phase. Predicting the integer ambiguity instead makes that identity a mathematical guarantee, not a hope.

How it works

AmbiguityNet outputs a continuous ambiguity prediction, rounds it via a straight-through estimator, and reconstructs phase directly:

φ̂ = ψ + 2π · round(k̂),   k̂ ∈ ℤ

That single design decision shapes everything downstream: training uses a physics-informed loss with a component that's provably near-zero regardless of prediction quality (documented explicitly, not hidden — see pyunwrap/models/losses.py); tiled inference merges the integer ambiguity map across overlapping patches, never the phase itself, because averaging phase directly across a tile boundary can silently produce a value that satisfies no physical interferogram.

Key features

Synthetic data engine (pyunwrap.synthetic) — Gaussian subsidence bowls, a from-scratch Okada (1985) rectangular fault dislocation model, a Mogi (1958) volcanic point source, DEM-driven topographic phase, Kolmogorov-spectrum atmospheric turbulence, orbital ramps, coherence-dependent decorrelation noise, and a pseudo-real strategy that rewraps real L-band (ALOS-2) unwrapped phase into simulated C-band data to help bridge the sim-to-real gap.

AmbiguityNet (pyunwrap.models) — a ResNet-34-encoder U-Net with a dual head: the integer ambiguity map via a straight-through-estimator rounding layer, and an auxiliary residue- probability map for uncertainty. Trained with a four-component physics-informed loss (ambiguity regression, re-wrap consistency, coherence-weighted smoothness, ambiguity-map residue penalty).

Curriculum training (pyunwrap.training) — a three-stage curriculum (high-coherence/low-gradient → moderate → full difficulty), optional SNAPHU pseudo-ground-truth fine-tuning on real data, AdamW with warmup/cosine annealing, and TensorBoard logging.

Production inference (pyunwrap.inference) — tiled processing of arbitrarily large interferograms with edge-aware, residue-probability-weighted smart merging of the ambiguity map, Monte Carlo Dropout uncertainty, and an ONNX Runtime → OpenVINO backend fallback chain for deployment without a PyTorch dependency.

Scientific analytics (pyunwrap.analytics) — Goldstein-style residue detection and clustering, Nyquist gradient-violation mapping, error-distribution statistics, Grad-CAM and Integrated-Gradients explainability, and uncertainty-calibration reliability diagrams.

Visualization & reporting (pyunwrap.visualization) — interactive Plotly 3D phase surfaces and heatmaps, a folium swipe-comparison map, and a self-contained, Jinja2-templated HTML report tying every stage together.

Architecture

InSARSyntheticGenerator ──┐                     real GeoTIFFs
                           │                     (wrapped, coherence, amplitude)
                           ▼                              │
                  tiling + normalization                  ▼
                           │                     tiled inference
                           ▼                     via PhaseUnwrapper
                  InSARTileDataset                         │
                           │                                │
                           ▼                                ▼
                   Trainer.fit()  ──── AmbiguityNet ──── smart ambiguity-map
                  (curriculum,          (this is the        merging (never
                   physics loss)        same model)          the phase)
                                                              │
                                                              ▼
                                                    unwrapped phase +
                                                    analytics + report

See docs/architecture.md for the full module-by-module reference and a longer explanation of the physics invariant every stage is built around.

Installation

git clone https://github.com/yourusername/pyunwrap.git
cd pyunwrap
pip install -e .

Optional extras, installed as needed:

pip install -e ".[dev,maps,deploy,notebooks]"   # everything

Quickstart

from pyunwrap.synthetic.generator import InSARSyntheticGenerator
from pyunwrap.models.ambiguity_net import AmbiguityNet
from pyunwrap.inference.unwrapper import PhaseUnwrapper

# Generate a synthetic training sample (Mogi volcanic source deformation).
gen = InSARSyntheticGenerator(size=256, seed=42)
sample = gen.generate_sample(deformation_type="mogi")

# Run tiled inference on a real interferogram with a trained model.
model = AmbiguityNet(pretrained=False, k_max=10.0)  # or load your own checkpoint
unwrapper = PhaseUnwrapper(model=model, device="cuda")
result = unwrapper.unwrap(
    wrapped_phase_path="data/wrapped_phase.tif",
    coherence_path="data/coherence.tif",
    amplitude_path="data/amplitude.tif",
    tile_size=512, overlap=64,
    generate_report=True,
)
result.save_geotiff("unwrapped_output.tif")

Training a model end to end:

pyunwrap-train \
  --train-hdf5 train_tiles.h5 --val-hdf5 val_tiles.h5 \
  --epochs 60 --warmup-epochs 5 \
  --finetune-hdf5 snaphu_pseudo_gt.h5 --finetune-start-epoch 55 \
  --out-dir runs/pyunwrap_v1

Notebooks

Two notebooks in notebooks/ walk through the package hands-on, checked in pre-executed with real outputs so they're readable without running anything:

• 01_training_pipeline.ipynb — the complete training chain: synthetic data, tiling, AmbiguityNet + Trainer, training curves, and predicted-vs-ground-truth comparison.
• 02_full_pipeline.ipynb — the complete end-to-end chain: a deformation-model gallery, tiling and augmentation visualized, a full curriculum training run with every loss component plotted, tiled inference on real GeoTIFFs, residue/Nyquist/error analytics, Grad-CAM and Integrated-Gradients explainability, 3D and interactive-map visualization, ONNX deployment with a numerical PyTorch-vs-ONNX agreement check, and the final HTML report.

pip install -e ".[dev,maps,notebooks]"
jupyter notebook notebooks/

Testing

pytest -m "not slow"              # fast unit + integration tests
pytest                             # full suite, including the end-to-end
                                    # synthetic → train → infer → report test
pytest --cov=pyunwrap --cov-report=term-missing

The suite includes known-answer physics tests (e.g. residue detection is checked against a hand-constructed phase vortex with an exact, known topological charge — not just "runs without crashing") and a tile-merging regression test that asserts tiled-and-merged inference is numerically identical to a whole-image pass, directly targeting the class of bug where tile boundaries silently corrupt the output. See CONTRIBUTING.md for the full breakdown and development setup.

Project status

Early-stage and actively developed. The pipeline — synthetic data generation, training, tiled inference, analytics, and reporting — is implemented and tested end to end, but no pretrained weights ship yet; from_pretrained() downloads from a Zenodo record you supply, and the example notebooks train small demo models from scratch rather than loading a benchmarked checkpoint. Monte Carlo Dropout uncertainty is currently a no-op (AmbiguityNet has no nn.Dropout layers yet, only BatchNorm) — tracked as a known gap, not hidden. APIs may change between minor versions until 1.0. See CHANGELOG.md for what's landed so far.

Relationship to the wider EO stack

pyunwrap works standalone, but is designed to eventually sit downstream of pygeofetch (interferogram acquisition/formation) and alongside ps-gnn (persistent scatterer identification) in a broader open-source InSAR processing stack.

Citation

If pyunwrap is useful in your research, please cite it:

@software{pyunwrap2026,
  title  = {pyunwrap: Physics-Informed Deep Learning for InSAR Phase Unwrapping},
  author = {{pyunwrap contributors}},
  year   = {2026},
  url    = {https://doi.org/10.5281/zenodo.22209219},
  note   = {Version 0.1.1}
}

References

• Goldstein, R. M., Zebker, H. A., & Werner, C. L. (1988). Satellite radar interferometry: Two-dimensional phase unwrapping. Radio Science, 23(4).
• Itoh, K. (1982). Analysis of the phase unwrapping algorithm. Applied Optics, 21(14).
• Chen, C. W., & Zebker, H. A. (2001). Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization (SNAPHU). JOSA A, 18(2).
• Okada, Y. (1985). Surface deformation due to shear and tensile faults in a half-space. Bulletin of the Seismological Society of America, 75(4).
• Mogi, K. (1958). Relations between the eruptions of various volcanoes and the deformations of the ground surfaces around them. Bulletin of the Earthquake Research Institute, 36.

Contributing

Contributions are welcome — bug reports, documentation, new deformation models, or core improvements. See CONTRIBUTING.md for development setup, test/lint conventions, and the design principles worth knowing before touching the physics-critical modules.

License

MIT