AI-generated Key Takeaways
-
Generates a square-shaped kernel with a specified radius for use in image processing.
-
The kernel can be defined in either pixels or meters, with meter-based kernels resizing based on zoom level.
-
Kernel values are normalized to sum to 1 by default, ensuring consistent results across different kernel sizes.
-
Users can control the kernel's magnitude by scaling each value, providing flexibility for various applications.
| Usage | Returns |
|---|---|
ee.Kernel.square(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 square kernel', ee.Kernel.square({radius: 3})); /** * Output weights matrix (up to 1/100 precision for brevity) * * [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] * [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] * [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] * [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] * [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] * [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] * [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] */
import ee import geemap.core as geemap
Colab (Python)
from pprint import pprint print('A square kernel:') pprint(ee.Kernel.square(**{'radius': 3}).getInfo()) # Output weights matrix (up to 1/100 precision for brevity) # [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] # [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] # [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] # [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] # [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] # [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] # [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]