ee.Kernel.gaussian

  • The ee.Kernel.gaussian function generates a Gaussian kernel from a sampled continuous Gaussian.

  • It requires a radius argument and offers optional arguments for sigma, units, normalize, and magnitude.

  • The function returns a Kernel object.

  • The examples demonstrate how to generate and print a Gaussian kernel in both JavaScript and Python.

 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)

display('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]