| framework: tensorflow |
| framework_detail: tensorflow_keras |
| weights_filename: model.weights.h5 |
| architecture: |
| name: resunet |
| implementation: src/colab_grd_gee/tf_model.py |
| input_shape: [128, 128, 2] |
| input_layout: NHWC |
| input_channels: 2 |
| input_order: [t1, t2] |
| output_channels: 2 |
| output_layout: NHWC |
| preprocessing: |
| gee_collection: COPERNICUS/S1_GRD |
| gee_instrument_mode: IW |
| gee_default_orbit_pass: ASCENDING |
| gee_default_polarization: VV |
| gee_composite: median |
| gee_download_scale_meters: 10 |
| gee_download_crs: EPSG:4326 |
| gee_download_scale: linear_sigma0 |
| gee_db_to_linear: "10 ** (db / 10)" |
| model_input_scale: linear_sigma0_from_gee |
| model_preprocessing: |
| - "convert linear sigma0 to dB with 10 * log10(linear + eps)" |
| - "clip dB values to [-20, 0]" |
| - "normalize clipped dB to [0, 1]" |
| db_min: -20.0 |
| db_max: 0.0 |
| channel_order: [t1, t2] |
| tiling: |
| patch_size: 128 |
| stride: 32 |
| batch_size: 8 |
| aggregation: kaiser |
| output: |
| name: predicted_coherence |
| selected_channel: 0 |
| range: [0.0, 1.0] |
| postprocessing: |
| - "select first model output channel" |
| - "aggregate overlapping patches" |
| - "clip predicted coherence to [0, 1]" |
| training_metadata: |
| source_weight_set: GEE |
| original_architecture_name: resunet |
| original_input_features: |
| - SigmaNoughtPrimary_VV |
| - SigmaNoughtSecondary_VV |
| original_output_features: |
| - Coherence_VV |
| - Uncertainty |
|
|
