AI-generated Key Takeaways
-
The
ee.Kernel.gaussian
function generates a Gaussian kernel from a sampled continuous Gaussian. -
It requires a
radius
argument and offers optional arguments forsigma
,units
,normalize
, andmagnitude
. -
The function returns a Kernel object.
-
The examples demonstrate how to generate and print a Gaussian kernel in both JavaScript and Python.
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] */
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]