beyond-backscatter-grd-gee

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.