ee.Kernel.chebyshev

Generates a distance kernel based on Chebyshev distance (greatest distance along any dimension).

UsageReturns
ee.Kernel.chebyshev(radius, units, normalize, magnitude)Kernel
ArgumentTypeDetails
radiusFloatThe radius of the kernel to generate.
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: falseNormalize the kernel values to sum to 1.
magnitudeFloat, default: 1Scale each value by this amount.

Examples

Code Editor (JavaScript)

print('A Chebyshev distance kernel', ee.Kernel.chebyshev({radius: 3}));

/**
 * Output weights matrix
 *
 * [3, 3, 3, 3, 3, 3, 3]
 * [3, 2, 2, 2, 2, 2, 3]
 * [3, 2, 1, 1, 1, 2, 3]
 * [3, 2, 1, 0, 1, 2, 3]
 * [3, 2, 1, 1, 1, 2, 3]
 * [3, 2, 2, 2, 2, 2, 3]
 * [3, 3, 3, 3, 3, 3, 3]
 */

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 Chebyshev distance kernel:')
pprint(ee.Kernel.chebyshev(**{'radius': 3}).getInfo())

#  Output weights matrix
#  [3, 3, 3, 3, 3, 3, 3]
#  [3, 2, 2, 2, 2, 2, 3]
#  [3, 2, 1, 1, 1, 2, 3]
#  [3, 2, 1, 0, 1, 2, 3]
#  [3, 2, 1, 1, 1, 2, 3]
#  [3, 2, 2, 2, 2, 2, 3]
#  [3, 3, 3, 3, 3, 3, 3]