| --- |
| 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. |
|
|
