SAR_3_band_model

Binary land/water semantic segmentation for Sentinel-1 SAR imagery. A U-Net with a ResNet-50 encoder, fine-tuned from the SSL4EO-S12 Sentinel-1 MOCO weights (ResNet50_Weights.SENTINEL1_GRD_MOCO) on a 3-band composite: VV, VH, VV−VH (dB).

Built for shoreline delineation: the water mask is vectorized and intersected with shore-normal transects to give a shoreline position per Sentinel-1 acquisition.

Model details

Architecture	U-Net decoder + ResNet-50 encoder (TorchGeo SemanticSegmentationTask)
Encoder init	ResNet50_Weights.SENTINEL1_GRD_MOCO (SSL4EO-S12, self-supervised)
Input	3 channels: VV, VH, VV−VH (dB scale, normalized)
Classes	0 = land, 1 = water (255 = ignore/nodata)
Training	50 epochs, cross-entropy, AdamW (lr 1e-3), batch 8, 512×512 chips, 16-mixed precision
Checkpoint	epoch=46-val_loss=0.0719
Format	ONNX, dynamic batch/height/width (~130 MB)

Performance

Held-out validation split (113 scenes, site-disjoint from training):

Overall acc.	Overall IoU	Water IoU	Land IoU
0.9724	0.9462	0.9571	0.9280

End-to-end shoreline accuracy against an independent optical shoreline dataset (16 US coastal sites, ~97,500 transect observations): RMSE 60.1 m, MAE 25.2 m, bias +9.8 m. Excluding two sites with known reference-data problems: RMSE 37.0 m, MAE 21.3 m.

This 3-band, no-speckle-filter configuration was the best of four variants tested (2-band vs 3-band × with/without a Refined Lee filter), winning 12 of 16 sites. Applying a speckle filter before the network consistently hurt accuracy.

Inputs and outputs

input   image       float32 [batch, 3, H, W]   (normalized)
output  logits      float32 [batch, 2, H, W]
        water_prob  float32 [batch, 1, H, W]   softmax(logits)[:, 1] = P(water)

H and W must be multiples of 32 (encoder stride). Pad bottom/right with 0.0 and crop the output back.

Classes

The model predicts two classes per pixel. The logits channel axis is in this order:

Index	Class	Meaning
0	land	Anything that is not open water: beach, dune, vegetation, buildings, bare soil, and exposed intertidal flats. Generally high or variable SAR backscatter.
1	water	Open water: ocean, bay, estuary, river, lake, and standing floodwater. Generally low SAR backscatter, because a smooth water surface reflects the radar pulse away from the sensor.

Take argmax(logits, axis=1) for a hard class map, or threshold water_prob (which is P(class 1)) at 0.5 for the same result with control over the operating point. Raise the threshold for a more conservative water mask, lower it to catch more wind-roughened water.

Because the classes are defined by backscatter, the land/water boundary this model finds is the instantaneous waterline at the time of acquisition, not a tidal datum. Correct for tide separately if you need a datum-based shoreline.

A third value, 255 = nodata/ignore, appears in the training labels and in the prediction rasters this project writes, but the model never outputs it. It marks pixels with no valid input: outside the scene footprint, or nodata/NaN in VV or VH. Carry your input validity mask through inference and stamp 255 into those pixels yourself after argmax.

⚠️ Preprocessing is NOT in the graph

The graph starts at the normalized tensor. Do this yourself, in order:

1. Read VV and VH in dB, tracking invalid pixels (nodata / NaN / ±Inf).

2. Derive VV−VH (dB difference) and stack as [VV, VH, VV−VH].

3. Fill invalid pixels with that channel's mean.

4. Normalize (x - mean) / std:

   Channel	mean	std
   VV	−12.59	5.26
   VH	−20.26	5.91
   VV−VH	10.5465	7.6855

5. Pad H/W up to a multiple of 32.

No speckle filter. This model was trained on raw dB, so do not apply Lee/Refined Lee before inference.

Preprocessing facts are also embedded in the ONNX metadata_props:

Training data

792 Sentinel-1 IW GRD scenes (10 m, VV+VH, dB) over US coastal sites: 679 train and 113 validation, split by site so no site appears in both. Labels are a random-forest water/land classification of Lee-ENL-filtered composites, manually reviewed and re-labelled for problematic sites.

Augmentation: random horizontal/vertical flips (p=0.5), random 90° rotations, Gaussian noise (σ=0.1). Scenes are randomly cropped/padded to 512×512 and never globally resized, which would smear SAR edges and corrupt categorical labels.

Limitations

• Sentinel-1 IW GRD in dB only. Linear amplitude/power, other sensors, or a different speckle-filter state will degrade results.
• Trained on US coastal sites; performance elsewhere (ice, very sheltered/vegetated water, extreme incidence angles) is untested.
• Wind-roughened water and radar shadow on steep terrain are the common failure modes for SAR water segmentation generally.
• Slight landward shoreline bias (~+10 m) relative to optical references.