ee.Kernel.gaussian

Generates a Gaussian kernel from a sampled continuous Gaussian.

Usage	Returns
ee.Kernel.gaussian(radius, sigma, units, normalize, magnitude)	Kernel

Argument	Type	Details
radius	Float	The radius of the kernel to generate.
sigma	Float, default: 1	Standard deviation of the Gaussian function (same units as radius).
units	String, 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.
normalize	Boolean, default: true	Normalize the kernel values to sum to 1.
magnitude	Float, default: 1	Scale each value by this amount.

Examples

Code Editor (JavaScript)Colab (Python)

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

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]