---
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.