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Creating a binary mask for pixels within each block of a raster.

Saisai

Hi all,

I am trying to create a binary mask for a single raster band in such a way that >75% of the pixels in each 3x3 block of the raster are regarded as valid pixels (1) and 25% or less are regarded as invalid (0).

Much like the above throughout the raster. My thought was to create a numpy mask array and then multiply that by an array of the raster itself but I'm not sure how to create a masked array with such specific details.

Any help would be greatly appreciated.

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Bsmith_DPW

Reclassify your 10m raster so every data cell has a value of 1.
Run Block Statistics on this reclassified raster, with a 3x3 rectangle snapped to your 30m raster and a sum statistic. This result tells you the number of 10m cells within each 30m cell.
Run Set Null on this block statistic result to remove all cells with a value less than 7.
Run Extract By Mask on your 30m raster using the result of the set null.

All comments

Saisai

Thank a lot Brennan, your solution was spot on.

Do you think it's possible to have a result on the number of cells that isn't a whole number?

Let say 7.5 or 8.5, that is remove all cells that are less than 7.5.

Bsmith_DPW

Reclassify your 10m raster so every data cell has a value of 1.
Run Block Statistics on this reclassified raster, with a 3x3 rectangle snapped to your 30m raster and a sum statistic. This result tells you the number of 10m cells within each 30m cell.
Run Set Null on this block statistic result to remove all cells with a value less than 7.
Run Extract By Mask on your 30m raster using the result of the set null.

Saisai

Hi,

Let me give some background on why I need the mask, I have 2 images the first being a 30m land cover image (whose pixels have been filtered earlier based on some conditions), and the 2nd a 10m sentinel image (single band) of the same region.

I need to extract pixels of the land cover image that are covered by 75-100% of the 10m image pixels, for instance there are (9) 10m pixels in each 30m land cover pixel in other areas especially around the edges the coverage may not be 100%, so the condition is to extract each land cover pixel there must be no less than (7) 10m pixels covering them.

I had thought of creating a mask of the 10m pixel image in the format described above which would help in extracting or filtering the 30m land cover pixels.

Thanks.

Bsmith_DPW

Within each 3x3 block of cells, what determines the <25% of cells to mask? Would this be random, or based on their value, or always mask the two black cells in your example?

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