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)

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]