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ee.Kernel.manhattan
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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
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]
*/
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
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]
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eGenerates a distance kernel based on the rectilinear (city-block) distance, also known as the Manhattan distance.\u003c/p\u003e\n"],["\u003cp\u003eThe kernel can be customized using parameters such as radius, units (pixels or meters), normalization, and magnitude scaling.\u003c/p\u003e\n"],["\u003cp\u003eBy default, the kernel uses pixels as units and is not normalized, with a magnitude of 1.\u003c/p\u003e\n"],["\u003cp\u003eThe output is a square matrix of weights representing the distances from the center pixel, as illustrated in the provided examples.\u003c/p\u003e\n"],["\u003cp\u003eThis kernel is commonly used in image processing for operations like edge detection and feature extraction, where rectilinear distances are relevant.\u003c/p\u003e\n"]]],["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"],null,["# ee.Kernel.manhattan\n\nGenerates a distance kernel based on rectilinear (city-block) distance.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------------------------------------------|---------|\n| `ee.Kernel.manhattan(radius, `*units* `, `*normalize* `, `*magnitude*`)` | Kernel |\n\n| Argument | Type | Details |\n|-------------|---------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `radius` | Float | The radius of the kernel to generate. |\n| `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. |\n| `normalize` | Boolean, default: false | Normalize the kernel values to sum to 1. |\n| `magnitude` | Float, default: 1 | Scale each value by this amount. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\nprint('A Manhattan kernel', ee.Kernel.manhattan({radius: 3}));\n\n/**\n * Output weights matrix\n *\n * [6, 5, 4, 3, 4, 5, 6]\n * [5, 4, 3, 2, 3, 4, 5]\n * [4, 3, 2, 1, 2, 3, 4]\n * [3, 2, 1, 0, 1, 2, 3]\n * [4, 3, 2, 1, 2, 3, 4]\n * [5, 4, 3, 2, 3, 4, 5]\n * [6, 5, 4, 3, 4, 5, 6]\n */\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\nfrom pprint import pprint\n\nprint('A Manhattan kernel:')\npprint(ee.Kernel.manhattan(**{'radius': 3}).getInfo())\n\n# Output weights matrix\n\n# [6, 5, 4, 3, 4, 5, 6]\n# [5, 4, 3, 2, 3, 4, 5]\n# [4, 3, 2, 1, 2, 3, 4]\n# [3, 2, 1, 0, 1, 2, 3]\n# [4, 3, 2, 1, 2, 3, 4]\n# [5, 4, 3, 2, 3, 4, 5]\n# [6, 5, 4, 3, 4, 5, 6]\n```"]]