How to set NoData value to isolated pixels in Python

Question

To reduce noise in a raster file, I aim to eliminate isolated pixels, those surrounded by NoData values in all eight neighboring positions.

I have this code, but it is not removing those isolated pixels, it removes some others, and I do not understand why:

import rasterio
from rasterio.enums import Resampling
from rasterio.fill import fillnodata

# Open the TIFF file
with rasterio.open('input.tif') as src:
    # Read the raster data
    data = src.read(1)
    nodata_value = src.nodata

    # Set the NoData pixels surrounded by 8 NoData pixels to the NoData value
    filled_data = fillnodata(data, mask=data == nodata_value, max_search_distance=1, smoothing_iterations=0)

    # Create a new TIFF file with the filled data
    with rasterio.open('output.tif', 'w', **src.profile) as dst:
        dst.write(filled_data, 1)

Answer 1

You could use rasterio.features.sieve. Based on the rasterio sieve example

import numpy as np
import rasterio
from rasterio.features import sieve


with rasterio.open('tests/data/shade.tif') as src:

    # Sieve out features 1 pixels or smaller.
    sieved = sieve(src, 1, out=np.zeros(src.shape, src.dtypes[0]))

    # Write out the sieved raster.
    kwargs = src.profile
    with rasterio.open('sieved.tif', 'w', **kwargs) as dst:
        dst.write(sieved, indexes=1)

Original

Sieved on top of original

For floating point data, you could do this:

import numpy as np
import rasterio
from rasterio.features import sieve


with rasterio.open('shade_float32.tif') as src:

    data = src.read(1, masked=True)

    # Invert the mask
    mask = (~data.mask).astype("uint8")

    # Sieve out features 1 pixel or smaller.
    sieved = sieve(mask, 1, out=np.zeros_like(mask))

    # Update the mask
    data.mask = ~(sieved.astype(bool))

    # Write out the sieved raster.
    kwargs = src.profile
    with rasterio.open('sieved.tif', 'w', **kwargs) as dst:
        dst.write(data, indexes=1)