AI-generated Key Takeaways
-
The
ee.Kernel.chebyshevfunction generates a distance kernel based on the Chebyshev distance. -
This function takes arguments for radius, units, normalize, and magnitude to customize the generated kernel.
-
The output weight matrix shows the calculated Chebyshev distances from the center of the kernel.
| Usage | Returns |
|---|---|
ee.Kernel.chebyshev(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 Chebyshev distance kernel' , ee . Kernel . chebyshev ({ radius : 3 })); /** * Output weights matrix * * [3, 3, 3, 3, 3, 3, 3] * [3, 2, 2, 2, 2, 2, 3] * [3, 2, 1, 1, 1, 2, 3] * [3, 2, 1, 0, 1, 2, 3] * [3, 2, 1, 1, 1, 2, 3] * [3, 2, 2, 2, 2, 2, 3] * [3, 3, 3, 3, 3, 3, 3] */
import ee import geemap.core as geemap
Colab (Python)
display ( 'A Chebyshev distance kernel:' , ee . Kernel . chebyshev ( ** { 'radius' : 3 })) # Output weights matrix # [3, 3, 3, 3, 3, 3, 3] # [3, 2, 2, 2, 2, 2, 3] # [3, 2, 1, 1, 1, 2, 3] # [3, 2, 1, 0, 1, 2, 3] # [3, 2, 1, 1, 1, 2, 3] # [3, 2, 2, 2, 2, 2, 3] # [3, 3, 3, 3, 3, 3, 3]
