resampling of 2D numpy array

There is a good tutorial on re-sampling using convolution here.

For integer factor up-scaling:

import numpy
import scipy
from scipy import ndimage, signal

# Scale factor
factor = 2

# Input image
a = numpy.arange(16).reshape((4,4))

# Empty image enlarged by scale factor
b = numpy.zeros((a.shape[0]*factor, a.shape[0]*factor))

# Fill the new array with the original values
b[::factor,::factor] = a

# Define the convolution kernel
kernel_1d = scipy.signal.boxcar(factor)
kernel_2d = numpy.outer(kernel_1d, kernel_1d)

# Apply the kernel by convolution, seperately in each axis
c = scipy.signal.convolve(b, kernel_2d, mode="valid")

Note that the factor can be different for each axis, and that you can also apply the convolution sequentially, on each axis. The kernels for bi-linear and bi-cubic are also shown in the link, with the bilinear interpolation making use of a triangular signal (scipy.signal.triang) and bi-cubic being a piece wise function.

You should also mind which portion of the interpolated image is valid; along the edges there is not sufficient support for the kernel.

Bi-cubic interpolation is the best option of the three, as far as satellite imagery goes.

There is a simpler solution for this https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.zoom.html.

Nearest neighbor interpolation is order=0, bilinear interpolation is order=1, and bicubic is order=3 (default).

import numpy as np
import scipy.ndimage

x = np.arange(6).reshape(3,2).astype(float)
z = (4/3, 3/2)
print('Original array:\n{0}\n\n'.format(x))
methods=['nearest-neighbor', 'bilinear', 'biquadratic', 'bicubic']
for o in range(4):
    print('Resampled with {0} interpolation:\n {1}\n\n'.
    format(methods[o],  scipy.ndimage.zoom(x, z, order=o)))

This results to:

Original array:
[[0. 1.]
 [2. 3.]
 [4. 5.]]


Resampled with nearest-neighbor interpolation:
 [[0. 1. 1.]
 [2. 3. 3.]
 [2. 3. 3.]
 [4. 5. 5.]]


Resampled with bilinear interpolation:
 [[0.         0.5        1.        ]
 [1.33333333 1.83333333 2.33333333]
 [2.66666667 3.16666667 3.66666667]
 [4.         4.5        5.        ]]


Resampled with biquadratic interpolation:
 [[1.04083409e-16 5.00000000e-01 1.00000000e+00]
 [1.11111111e+00 1.61111111e+00 2.11111111e+00]
 [2.88888889e+00 3.38888889e+00 3.88888889e+00]
 [4.00000000e+00 4.50000000e+00 5.00000000e+00]]


Resampled with bicubic interpolation:
 [[5.55111512e-16 5.00000000e-01 1.00000000e+00]
 [1.03703704e+00 1.53703704e+00 2.03703704e+00]
 [2.96296296e+00 3.46296296e+00 3.96296296e+00]
 [4.00000000e+00 4.50000000e+00 5.00000000e+00]]