ee.Kernel.manhattan
Generates a distance kernel based on rectilinear (city-block) distance.
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
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]
*/
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 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]
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[[["Generates a distance kernel based on the rectilinear (city-block) distance, also known as the Manhattan distance."],["The kernel can be customized using parameters such as radius, units (pixels or meters), normalization, and magnitude scaling."],["By default, the kernel uses pixels as units and is not normalized, with a magnitude of 1."],["The output is a square matrix of weights representing the distances from the center pixel, as illustrated in the provided examples."],["This kernel is commonly used in image processing for operations like edge detection and feature extraction, where rectilinear distances are relevant."]]],["This tool generates a rectilinear (city-block) distance kernel using `ee.Kernel.manhattan`. Key actions involve setting the `radius`, specifying `units` as pixels or meters, and optionally `normalize` the kernel to sum to 1, and `magnitude` to scale each value. The kernel's output is a matrix, where each cell's value represents its distance.\n"]]