ee.Kernel.gaussian

  • The ee.Kernel.gaussian function generates a Gaussian kernel, which is essentially a matrix of weights used for image processing, derived from a continuous Gaussian distribution.

  • Users can customize the kernel by defining its radius, standard deviation (sigma), units (pixels or meters), normalization, and magnitude (scaling factor).

  • By default, the kernel is normalized, meaning the sum of its values equals 1, and has a magnitude of 1, applying no scaling to the pixel values.

  • The generated Gaussian kernel can be applied to imagery to perform various operations such as blurring or smoothing, as demonstrated in the example code snippets.

Generates a Gaussian kernel from a sampled continuous Gaussian.

UsageReturns
ee.Kernel.gaussian(radius, sigma, units, normalize, magnitude)Kernel
ArgumentTypeDetails
radiusFloatThe radius of the kernel to generate.
sigmaFloat, default: 1Standard deviation of the Gaussian function (same units as radius).
unitsString, default: "pixels"The system of measurement for the kernel ('pixels' or 'meters'). If the kernel is specified in meters, it will resize when the zoom-level is changed.
normalizeBoolean, default: trueNormalize the kernel values to sum to 1.
magnitudeFloat, default: 1Scale each value by this amount.

Examples

Code Editor (JavaScript)

print('A Gaussian kernel', ee.Kernel.gaussian({radius: 3}));

/**
 * Output weights matrix (up to 1/1000 precision for brevity)
 *
 * [0.002, 0.013, 0.021, 0.013, 0.002]
 * [0.013, 0.059, 0.098, 0.059, 0.013]
 * [0.021, 0.098, 0.162, 0.098, 0.021]
 * [0.013, 0.059, 0.098, 0.059, 0.013]
 * [0.002, 0.013, 0.021, 0.013, 0.002]
 */

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

from pprint import pprint

print('A Gaussian kernel:')
pprint(ee.Kernel.gaussian(**{'radius': 3}).getInfo())

#  Output weights matrix (up to 1/1000 precision for brevity)

#  [0.002, 0.013, 0.021, 0.013, 0.002]
#  [0.013, 0.059, 0.098, 0.059, 0.013]
#  [0.021, 0.098, 0.162, 0.098, 0.021]
#  [0.013, 0.059, 0.098, 0.059, 0.013]
#  [0.002, 0.013, 0.021, 0.013, 0.002]