从具有特定范围值的光栅图像中删除像素

Question

update: any idea how to delete pixel from specific range value raster image with using numpy/scipy or gdal ?

or how to can create new raster with some class using raster calculation expressions (better)

for example i have a raster image with the 5 class:

1. 0-100 
2. 100-200 
3. 200-300 
4. 300-500 
5. 500-1000 

and i want to delete class 1 range value or maybe class 1,2,4,5

i begin with this script:

import numpy as np
from osgeo import gdal
ds = gdal.Open("raster3.tif")
myarray = np.array(ds.GetRasterBand(1).ReadAsArray())
#print myarray.shape
#print myarray.size
#print myarray
new=np.delete(myarray[::2], 1)

but i cant to complete

the image White in class 5 and black class 1

Answer 1

Rasters are 2-D arrays of values, with each value being stored in a pixel (which stands for picture element). Each pixel must contain some information. It is not possible to delete or remove pixels from the array because rasters are usually encoded as a simple 1-dimensional string of bits. Metadata commonly helps explain where line breaks are and the length of the bitstring, so that the 1-D bitstring can be understood as a 2-D array. If you "remove" a pixel, then you break the raster. The 2-D grid is no longer valid.

Of course, there are many instances where you do want to effectively discard or clean the raster of data. Such an example might be to remove pixels that cover land from a raster of sea-surface temperatures. To accomplish this goal, many geospatial raster formats hold metadata describing what are called NoData values. Pixels containing a NoData value are interpreted as not existing. Recall that in a raster, each pixel must contain some information. The NoData paradigm allows the structure and format of rasters to be met, while also giving a way to mask pixels from being displayed or analyzed. There is still data (bits, 1s and 0s) at the masked pixels, but it only serves to identify the pixel as invalid.

With this in mind, here is an example using gdal which will mask values in the range of 0-100 so they are NoData, and "do not exist". The NoData value will be specified as 0.

from osgeo import gdal

# open dataset to read, and get a numpy array
ds = gdal.Open("raster3.tif", 'r')
myarray = ds.GetRasterBand(1).ReadAsArray()
# modify numpy array to mask values
myarray[myarray <= 100] = 0

# open output dataset, which is a copy of original
driver = gdal.GetDriverByName('GTiff')
ds_out = driver.CreateCopy("raster3_with_nodata.tif", ds)
# write the modified array to the raster
ds_out.GetRasterBand(1).WriteArray(myarray)
# set the NoData metadata flag
ds_out.GetRasterBand(1).SetNoDataValue(0)
# clear the buffer, and ensure file is written
ds_out.FlushCache()

Answer 2

I want to contribute with an example for landsat data. In this qick guide, you will be able to exclude Landsat cloud pixels. Landsat offers the Quality Assessment Band (BQA), which includes int32 values (classes) regarding natural features such as Clouds, Rocks, Ice, Water, Cloud Shadow etc.

We will use the BQA to clip the cloud pixels in the other bands.

# Import Packages
import rasterio as rio
import earthpy.plot as ep
from matplotlib import pyplot
import rioxarray as rxr
from numpy import ma

# Open the Landsat Band 3
Landsat_Image = rxr.open_rasterio(r"C:\...\LC08_L1TP_223075_20210311_20210317_01_T1_B3.tif")

# Open the Quality Assessment Band 
BQA = rxr.open_rasterio(r"C:\...\LC08_L1TP_223075_20210311_20210317_01_T1_BQA.tif").squeeze()

# Create a list with the QA values that represent cloud, cloud_shadow, etc.
Cloud_Values = [6816, 6848, 6896, 7072]

# Mask the data using the pixel QA layer
landsat_masked = Landsat_Image.where(~BQA.isin(Cloud_Values))
landsat_masked

# Plot the masked data
landsat_masked_plot = ma.masked_array(landsat_masked.values,landsat_masked.isnull())

# Plot
ep.plot_rgb(landsat_masked_plot, rgb=[2, 1, 0], title = "Masked Data")
plt.show()
###############################################################################
# Export the masked Landsat Scenes to Directory "Masked_Bands_QA"
out_img = landsat_masked
out_img.shape
out_transform = landsat_masked.rio.transform()

# Get a Band of the same Scene for reference
rastDat = rio.open(r"C:\Dados_Espaciais\NDVI_Usinas\Adeco\Indices\Imagens\LC08_L1TP_223075_20210311_20210317_01_T1\LC08_L1TP_223075_20210311_20210317_01_T1_B3.tif")


#copying metadata from original raster
out_meta = rastDat.meta.copy()

#amending original metadata
out_meta.update({'nodata': 0,
                 'height' : out_img.shape[1],
                 'width' : out_img.shape[2],
                 'transform' : out_transform}) 

# writing and then re-reading the output data to see if it looks good
for i in range(out_img.shape[0]):
    with rio.open(rf"C:\Dados_Espaciais\DSM\Bare_Soil_Landsat\Teste_{i+1}_masked.tif",'w',**out_meta) as dst:
        dst.write(out_img[i,:,:],1)

This way you tell the program: Check the areas in BQA with these "Cloud_Values" and exclude these areas, but in the landsat image that I provided. I hope it works.