AI-generated Key Takeaways
-
The
ee.Kernel.plusfunction generates a plus-shaped boolean kernel. -
It requires a
radiusargument and optionally acceptsunits,normalize, andmagnitude. -
The function returns a Kernel object.
| Usage | Returns |
|---|---|
ee.Kernel.plus(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: 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 plus kernel' , ee . Kernel . plus ({ radius : 3 })); /** * Output weights matrix (1/1000 precision shown for brevity) * * [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] * [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] * [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] * [0.076, 0.076, 0.076, 0.076, 0.076, 0.076, 0.076] * [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] * [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] * [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] */
import ee import geemap.core as geemap
Colab (Python)
display ( 'A plus kernel:' , ee . Kernel . plus ( ** { 'radius' : 3 })) # Output weights matrix (1/1000 precision shown for brevity) # [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] # [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] # [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] # [0.076, 0.076, 0.076, 0.076, 0.076, 0.076, 0.076] # [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] # [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] # [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000]
