ee.Kernel.plus
Generates a plus-shaped boolean kernel.
| 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
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]
*/
Python setup
See the
Python Environment page for information on the Python API and using
geemap for interactive development.
import ee
import geemap.core as geemap
from pprint import pprint
print('A plus kernel:')
pprint(ee.Kernel.plus(**{'radius': 3}).getInfo())
# 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]
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[[["Generates a plus-shaped kernel (a matrix of weights) for image processing."],["The kernel's radius, units of measurement, normalization, and magnitude can be customized."],["By default, the kernel values are normalized to sum to 1 and have a magnitude of 1."],["This kernel can be applied to images to perform operations like edge detection or feature extraction where a cross-shaped neighborhood is relevant."]]],["A plus-shaped kernel is generated using `ee.Kernel.plus`, taking a `radius` to define its size. The `units` parameter, defaulting to 'pixels', can be set to 'meters' for zoom-responsive sizing. The kernel values can be normalized to sum to 1 with the `normalize` parameter (default true) or scaled by a `magnitude`. The kernel output is shown as a matrix with weighted values.\n"]]