AI-generated Key Takeaways
-
The
ee.Kernel.crossfunction generates a cross-shaped boolean kernel. -
The function takes arguments for
radius,units,normalize, andmagnitude. -
The generated kernel is a weights matrix representing the cross shape.
-
Examples are provided for both JavaScript and Python environments.
| Usage | Returns |
|---|---|
ee.Kernel.cross(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 cross kernel' , ee . Kernel . cross ({ radius : 3 })); /** * Output weights matrix (up to 1/1000 precision for brevity) * * [0.076, 0.000, 0.000, 0.000, 0.000, 0.000, 0.076] * [0.000, 0.076, 0.000, 0.000, 0.000, 0.076, 0.000] * [0.000, 0.000, 0.076, 0.000, 0.076, 0.000, 0.000] * [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] * [0.000, 0.000, 0.076, 0.000, 0.076, 0.000, 0.000] * [0.000, 0.076, 0.000, 0.000, 0.000, 0.076, 0.000] * [0.076, 0.000, 0.000, 0.000, 0.000, 0.000, 0.076] */
import ee import geemap.core as geemap
Colab (Python)
display ( 'A cross kernel:' , ee . Kernel . cross ( ** { 'radius' : 3 })) # Output weights matrix (up to 1/1000 precision for brevity) # [0.076, 0.000, 0.000, 0.000, 0.000, 0.000, 0.076] # [0.000, 0.076, 0.000, 0.000, 0.000, 0.076, 0.000] # [0.000, 0.000, 0.076, 0.000, 0.076, 0.000, 0.000] # [0.000, 0.000, 0.000, 0.076, 0.000, 0.000, 0.000] # [0.000, 0.000, 0.076, 0.000, 0.076, 0.000, 0.000] # [0.000, 0.076, 0.000, 0.000, 0.000, 0.076, 0.000] # [0.076, 0.000, 0.000, 0.000, 0.000, 0.000, 0.076]
