Page Summary
-
The
ee.Kernel.euclideanfunction generates a distance kernel based on Euclidean (straight-line) distance. -
It requires a
radiusargument and optionally acceptsunits,normalize, andmagnitudeparameters. -
The function returns a Kernel object.
-
Examples are provided for both JavaScript and Python, showing how to use the function and the resulting weight matrix.
| Usage | Returns |
|---|---|
ee.Kernel.euclidean(radius, units
, normalize
, magnitude
)
|
Kernel |
| Argument | Type | Details |
|---|---|---|
radius
|
Float | The radius of the kernel to generate. |
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: false | Normalize the kernel values to sum to 1. |
magnitude
|
Float, default: 1 | Scale each value by this amount. |
Examples
Code Editor (JavaScript)
print ( 'A Euclidean distance kernel' , ee . Kernel . euclidean ({ radius : 3 })); /** * Output weights matrix (up to 1/1000 precision for brevity) * * [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242] * [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605] * [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162] * [3.000, 2.000, 1.000, 0.000, 1.000, 2.000, 3.000] * [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162] * [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605] * [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242] */
import ee import geemap.core as geemap
Colab (Python)
display ( 'A Euclidean distance kernel:' , ee . Kernel . euclidean ( ** { 'radius' : 3 })) # Output weights matrix (up to 1/1000 precision for brevity) # [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242] # [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605] # [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162] # [3.000, 2.000, 1.000, 0.000, 1.000, 2.000, 3.000] # [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162] # [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605] # [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]
