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ee.Kernel.chebyshev
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Generates a distance kernel based on Chebyshev distance (greatest distance along any dimension).
| Usage | Returns | ee.Kernel.chebyshev(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 Chebyshev distance kernel', ee.Kernel.chebyshev({radius: 3}));
/**
* Output weights matrix
*
* [3, 3, 3, 3, 3, 3, 3]
* [3, 2, 2, 2, 2, 2, 3]
* [3, 2, 1, 1, 1, 2, 3]
* [3, 2, 1, 0, 1, 2, 3]
* [3, 2, 1, 1, 1, 2, 3]
* [3, 2, 2, 2, 2, 2, 3]
* [3, 3, 3, 3, 3, 3, 3]
*/
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 Chebyshev distance kernel:')
pprint(ee.Kernel.chebyshev(**{'radius': 3}).getInfo())
# Output weights matrix
# [3, 3, 3, 3, 3, 3, 3]
# [3, 2, 2, 2, 2, 2, 3]
# [3, 2, 1, 1, 1, 2, 3]
# [3, 2, 1, 0, 1, 2, 3]
# [3, 2, 1, 1, 1, 2, 3]
# [3, 2, 2, 2, 2, 2, 3]
# [3, 3, 3, 3, 3, 3, 3]
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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 Chebyshev distance, which calculates the greatest distance along any dimension between two pixels.\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\u003eWhen applied, the kernel assigns weights to neighboring pixels based on their Chebyshev distance from the central pixel, creating a matrix of weights.\u003c/p\u003e\n"],["\u003cp\u003eThe resulting weights matrix can be used in various image processing operations, such as smoothing or neighborhood analysis.\u003c/p\u003e\n"]]],["A Chebyshev distance kernel is generated using `ee.Kernel.chebyshev()` with a specified `radius`. The measurement system can be set to 'pixels' or 'meters' via the `units` argument. The kernel values can be normalized to sum to 1 using `normalize`, and scaled with `magnitude`. The output is a kernel representing the Chebyshev distance, where the greatest distance along any dimension defines the value, and it is presented as a matrix.\n"],null,["# ee.Kernel.chebyshev\n\nGenerates a distance kernel based on Chebyshev distance (greatest distance along any dimension).\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------------------------------------------|---------|\n| `ee.Kernel.chebyshev(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 Chebyshev distance kernel', ee.Kernel.chebyshev({radius: 3}));\n\n/**\n * Output weights matrix\n *\n * [3, 3, 3, 3, 3, 3, 3]\n * [3, 2, 2, 2, 2, 2, 3]\n * [3, 2, 1, 1, 1, 2, 3]\n * [3, 2, 1, 0, 1, 2, 3]\n * [3, 2, 1, 1, 1, 2, 3]\n * [3, 2, 2, 2, 2, 2, 3]\n * [3, 3, 3, 3, 3, 3, 3]\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 Chebyshev distance kernel:')\npprint(ee.Kernel.chebyshev(**{'radius': 3}).getInfo())\n\n# Output weights matrix\n# [3, 3, 3, 3, 3, 3, 3]\n# [3, 2, 2, 2, 2, 2, 3]\n# [3, 2, 1, 1, 1, 2, 3]\n# [3, 2, 1, 0, 1, 2, 3]\n# [3, 2, 1, 1, 1, 2, 3]\n# [3, 2, 2, 2, 2, 2, 3]\n# [3, 3, 3, 3, 3, 3, 3]\n```"]]