AI-generated Key Takeaways
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Generates a distance kernel based on the rectilinear (city-block) distance, also known as the Manhattan distance.
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The kernel can be customized using parameters such as radius, units (pixels or meters), normalization, and magnitude scaling.
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By default, the kernel uses pixels as units and is not normalized, with a magnitude of 1.
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The output is a square matrix of weights representing the distances from the center pixel, as illustrated in the provided examples.
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This kernel is commonly used in image processing for operations like edge detection and feature extraction, where rectilinear distances are relevant.
| Usage | Returns |
|---|---|
ee.Kernel.manhattan(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 Manhattan kernel', ee.Kernel.manhattan({radius: 3})); /** * Output weights matrix * * [6, 5, 4, 3, 4, 5, 6] * [5, 4, 3, 2, 3, 4, 5] * [4, 3, 2, 1, 2, 3, 4] * [3, 2, 1, 0, 1, 2, 3] * [4, 3, 2, 1, 2, 3, 4] * [5, 4, 3, 2, 3, 4, 5] * [6, 5, 4, 3, 4, 5, 6] */
import ee import geemap.core as geemap
Colab (Python)
from pprint import pprint print('A Manhattan kernel:') pprint(ee.Kernel.manhattan(**{'radius': 3}).getInfo()) # Output weights matrix # [6, 5, 4, 3, 4, 5, 6] # [5, 4, 3, 2, 3, 4, 5] # [4, 3, 2, 1, 2, 3, 4] # [3, 2, 1, 0, 1, 2, 3] # [4, 3, 2, 1, 2, 3, 4] # [5, 4, 3, 2, 3, 4, 5] # [6, 5, 4, 3, 4, 5, 6]