Use the model

You can use this model in the Classify Pixels Using Deep Learning tool available in the Image Analyst toolbox in ArcGIS Pro.

Recommended imagery configuration

The recommended imagery configuration is as follows:

• Resolution—10 meters
• Dynamic range—8 bit
• Bands—Three-band Sentinel-1 C band SAR GRD VH polarization band raster

Extract water bodies

Complete the following steps to extract water bodies from the imagery:

1. Open ArcGIS Pro, and create an ArcGIS Pro project.
2. In the Catalog pane, browse to the SAR data. Expand the Sentinel-1 GRDH data folder (.SAFE), right-click the manifest.safe file, and select Add To Current Map.

   Note:

   You can download the imagery from Copernicus Data Space Ecosystem or Sentinel Hub.

   

3. Download the Water Body Extraction (SAR)—USA model.
4. Zoom to an area of interest.

   

The manifest.safe raster comprises two bands: VV and VH. The model operates with the VH polarized band.
5. To extract the VH band from the manifest.safe file, use the Extract Bands raster function. On the General tab, set the variables as follows:
   1. For Name, use the default value.
   2. For Output Pixel Type, select 8 Bit Unsigned from the drop-down list.

   

6. On the Parameters tab, set the variables as follows:
   1. For Raster, select the IW_manifest raster added to the map or select the manifest.safe file from the source data folder.
   2. For Method, select the Band IDs option.

      For Combination, in the manifest.safe raster band, 2 represents the VH band. The model is compatible with a three-band composite of the VH band.

   3. To create a three-band composite, type 2 2 2.

   

7. Browse to Tools under the Analysis tab.

   

8. Click the Toolboxes tab in the Geoprocessing pane, select Image Analyst Tools and browse to the Classify Pixels Using Deep Learning tool under Deep Learning.

   

9. On the Parameters tab, set the variables as follows:
   1. For Input Raster, select the three-band Sentinel-1 GRD VH polarization imagery layer.
   2. For Output Classified Raster, set the output raster that will contain the classification results as the binary raster representing water and nonwater classes.
   3. Optionally, for Model Definition, select the pretrained or fine-tuned model .dlpk file.
   4. Optionally, for Arguments, change the values of the arguments if required.

   

10. Set the variables under the Environments tab as follows:
    1. For Processing Extent, select Current Display Extent or any other option from the drop-down menu.
    2. For Cell Size, change if required.

       Ten meters is the expected SAR image resolution.

    3. For Processor Type, select CPU or GPU as needed.
       If GPU is available, it is recommended that you select GPU and set GPU ID to specify the GPU to be used.

    

11. Click Run.

    Once processing is complete, the output classified raster is added to the map.