AI-generated Key Takeaways
-
The
ee.Kernel.gaussianfunction generates a Gaussian kernel from a sampled continuous Gaussian. -
It requires a
radiusargument 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)
display ( '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]
