[GIS] QGIS Raster map, smoothing pixels

qgisrastersmoothing

I am trying to generate a map in Qgis which makes it possible to see the temperature of a region during a heatwave. This was a great succes. However, I want to add shades as well, since shades lower the temperature.

Now I have the following map:

On this map we see a part of a city, with red being warmer and blue beging colder. Tree shades for instance make some parts of a street cooler. As you can see some area's have become very pixel-ish. I would like to smooth this map a bit so that for instance one orange pixel within a group of blue pixels, takes over the value of its neighbour and turn into blue as well.

Does anybody has an idea for this?

Best Answer

After some googling I found the SAGA Gaussian Filter in the qgis toolbox. After filtering the raster, a much smoother raster came out.

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[GIS] What raster smoothing/generalization tools are available

I've been exploring SciPy's signal.convolve approach (based on this cookbook), and am having some really nice success with the following snippet:

import numpy as np
from scipy.signal import fftconvolve

def gaussian_blur(in_array, size):
    # expand in_array to fit edge of kernel
    padded_array = np.pad(in_array, size, 'symmetric')
    # build kernel
    x, y = np.mgrid[-size:size + 1, -size:size + 1]
    g = np.exp(-(x**2 / float(size) + y**2 / float(size)))
    g = (g / g.sum()).astype(in_array.dtype)
    # do the Gaussian blur
    return fftconvolve(padded_array, g, mode='valid')

I use this in another function which reads/writes float32 GeoTIFFs via GDAL (no need to rescale to 0-255 byte for image processing), and I've been using attempting pixel sizes (e.g., 2, 5, 20) and it has really nice output (visualized in ArcGIS with 1:1 pixel and constant min/max range):

Gaussian DTM

Note: this answer was updated to use a much faster FFT-based signal.fftconvolve processing function.

[GIS] Smoothing raster map using R

Here are some ideas.

With base plot you can do

 plot(x, interpolate=TRUE)

You can also resample your data

 y <- disaggregate(x, 5, method='bilinear')

Or indeed smooth it using a focal operation

 y <- focal(x, w=matrix(1, 5, 5), mean)

Or a combination

 y <- disaggregate(x, 5)
 y <- focal(y, w=matrix(1, 5, 5), mean)

The question whether doing this is a good idea or not is another matter, that I'll leave to you to decide

Best Answer

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[GIS] What raster smoothing/generalization tools are available

[GIS] Smoothing raster map using R

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