AI-generated Key Takeaways
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Generates a kernel with values arranged in a cross shape, useful for image processing operations.
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The kernel's size is determined by the
radius
parameter, and values can be normalized and scaled. -
By default, the kernel values are normalized to sum to 1 and scaled by 1.
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The kernel can be defined in either 'pixels' or 'meters', affecting its behavior when the zoom level changes.
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)
from pprint import pprint print('A cross kernel:') pprint(ee.Kernel.cross(**{'radius': 3}).getInfo()) # 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]