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library_name: keras
tags:
- remote-sensing
- sar
- sentinel-1
- coherence
- earth-engine
---
# Beyond Backscatter GRD/GEE Coherence Estimator
This model package contains the TensorFlow/Keras GRD/GEE model weights for **Beyond Backscatter: InSAR Coherence from Detected SAR Images**.
GitHub repository:
https://github.com/FPSica/BeyondBackscatter
Public Colab notebook:
https://colab.research.google.com/github/FPSica/BeyondBackscatter/blob/main/notebooks/back2coh_grd_gee_colab.ipynb
## Task
Predict an InSAR-like coherence map from two detected Sentinel-1 GRD/GEE SAR backscatter images.
## Inputs
The public notebook downloads two Sentinel-1 GRD sigma0 backscatter images from Google Earth Engine in linear scale. The default polarization is `VV`.
Earth Engine preprocessing:
- collection: `COPERNICUS/S1_GRD`;
- acquisition mode: `IW`;
- orbit pass filtering, default `ASCENDING`;
- optional relative orbit filtering;
- two user-selected date windows;
- median composite for each date window;
- dB-to-linear conversion using `10 ** (db / 10)`;
- selected polarization;
- clipped region of interest;
- 10 m output scale by default.
Model preprocessing:
- convert downloaded linear sigma0 back to dB with `10 * log10(linear + eps)`;
- clip to `[-20, 0]` dB;
- normalize to `[0, 1]`;
- channel order: `[t1, t2]`;
- tiled inference with 128 x 128 patches and Kaiser-window aggregation.
## Outputs
The model outputs a predicted coherence map in `[0, 1]`. The public notebook saves the map as GeoTIFF, PNG, and NumPy products, preserving georeferencing from the downloaded Sentinel-1 inputs.
## Files
- `model.weights.h5`: real GRD/GEE TensorFlow/Keras weights in legacy Keras H5 format.
- `config.yaml`: model, preprocessing, tiling, and output conventions.
- `model_metadata.json`: lightweight public packaging metadata.
The TensorFlow/Keras architecture implementation is provided by the GitHub repository in `src/colab_grd_gee/tf_model.py`.
## Limitations
- This is not the SLC-based workflow.
- This is not true interferometric processing from complex SLC data.
- The pseudo-RGB products produced by the notebook are SAR/coherence visualizations, not optical imagery.
- Earth Engine authentication and a valid Earth Engine-enabled Google Cloud project are required to run the full notebook.
- Start with a small ROI before processing larger areas.
## Citation
Beyond Backscatter: InSAR Coherence from Detected SAR Images
Francescopaolo Sica, Andrea Pulella, Michael Schmitt
## License
The GitHub code repository is MIT licensed. The model-weight license should be confirmed by the authors before redistribution or downstream release.
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