Difference between revisions of "Exclusion Mask"

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For its generation, we implement the methods proposed in section 1, following the identified problems of SAR-based flood mapping. <br>
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For its generation, we implement the methods proposed '''in section 1''', following the identified problems of SAR-based flood mapping. <br>
 
The parameter database stores for all locations on pixel basis the areas excluded by the four effect groups, with the radar shadow layer per local Sentinel-1 orbit configurations (up to six per location). During NRT operation, the relative orbit is determined from the S-1 metadata, and the respective Exclusion Mask layers are subset to the extent of the processed Sentinel-1 scene and form a single binary mask for exclusion areas.  The Exclusion Mask layer values are described below.
 
The parameter database stores for all locations on pixel basis the areas excluded by the four effect groups, with the radar shadow layer per local Sentinel-1 orbit configurations (up to six per location). During NRT operation, the relative orbit is determined from the S-1 metadata, and the respective Exclusion Mask layers are subset to the extent of the processed Sentinel-1 scene and form a single binary mask for exclusion areas.  The Exclusion Mask layer values are described below.
  

Latest revision as of 13:09, 15 March 2021

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The Exclusion Mask indicates the pixel locations where the SAR data could not deliver the necessary information for a robust flood delineation. It combines static effects leading to

  1. no-sensitivity in flood mapping
  2. water-look-alikes
  3. strong topography
  4. radar shadows

For its generation, we implement the methods proposed in section 1, following the identified problems of SAR-based flood mapping.
The parameter database stores for all locations on pixel basis the areas excluded by the four effect groups, with the radar shadow layer per local Sentinel-1 orbit configurations (up to six per location). During NRT operation, the relative orbit is determined from the S-1 metadata, and the respective Exclusion Mask layers are subset to the extent of the processed Sentinel-1 scene and form a single binary mask for exclusion areas. The Exclusion Mask layer values are described below.


Figure5.jpg


Note

As no-sensitivity is a problem leading often to an underestimation of floods rather than to overestimation (e.g. in urban areas), the no-sensitivity -masking is only applied to pixels that are classified as non-flooded, whereas pixels classified as flooded are kept un-masked. Any no-data areas from the flood algorithm are forwarded to this layer and added as no-data values.



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