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ee.Kernel.euclidean
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Genera un kernel de distancia basado en la distancia euclidiana (en línea recta).
| Uso | Muestra |
|---|
ee.Kernel.euclidean(radius, units, normalize, magnitude) | Kernel |
| Argumento | Tipo | Detalles |
|---|
radius | Número de punto flotante | Es el radio del kernel que se generará. |
units | Cadena, valor predeterminado: "pixels" | Es el sistema de medición del kernel ("píxeles" o "metros"). Si el kernel se especifica en metros, cambiará de tamaño cuando se modifique el nivel de zoom. |
normalize | Booleano, valor predeterminado: falso | Normaliza los valores del kernel para que sumen 1. |
magnitude | Número de punto flotante, valor predeterminado: 1 | Cada valor se multiplica por esta cantidad. |
Ejemplos
Editor de código (JavaScript)
print('A Euclidean distance kernel', ee.Kernel.euclidean({radius: 3}));
/**
* Output weights matrix (up to 1/1000 precision for brevity)
*
* [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]
* [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]
* [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]
* [3.000, 2.000, 1.000, 0.000, 1.000, 2.000, 3.000]
* [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]
* [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]
* [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]
*/
Configuración de Python
Consulta la página
Entorno de Python para obtener información sobre la API de Python y el uso de geemap para el desarrollo interactivo.
import ee
import geemap.core as geemap
Colab (Python)
from pprint import pprint
print('A Euclidean distance kernel:')
pprint(ee.Kernel.euclidean(**{'radius': 3}).getInfo())
# Output weights matrix (up to 1/1000 precision for brevity)
# [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]
# [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]
# [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]
# [3.000, 2.000, 1.000, 0.000, 1.000, 2.000, 3.000]
# [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]
# [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]
# [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]
Salvo que se indique lo contrario, el contenido de esta página está sujeto a la licencia Atribución 4.0 de Creative Commons, y los ejemplos de código están sujetos a la licencia Apache 2.0. Para obtener más información, consulta las políticas del sitio de Google Developers. Java es una marca registrada de Oracle o sus afiliados.
Última actualización: 2025-07-26 (UTC)
[null,null,["Última actualización: 2025-07-26 (UTC)"],[[["\u003cp\u003eGenerates a kernel to weight pixels based on their straight-line distance from the center.\u003c/p\u003e\n"],["\u003cp\u003eKernel values represent the Euclidean distance from the center pixel, optionally normalized and scaled.\u003c/p\u003e\n"],["\u003cp\u003eThe radius of the kernel and units of measurement (pixels or meters) are configurable.\u003c/p\u003e\n"],["\u003cp\u003eWhen specified in meters, the kernel automatically resizes with zoom level changes.\u003c/p\u003e\n"]]],["The `ee.Kernel.euclidean` function generates a distance kernel based on Euclidean distance, returning a Kernel object. Key parameters include `radius`, determining the kernel's size; `units` (\"pixels\" or \"meters\"), dictating the measurement system; `normalize` (default: false), setting whether values sum to 1; and `magnitude` (default: 1), scaling values. An example kernel with a radius of 3 is demonstrated, illustrating the output weight matrix.\n"],null,["# ee.Kernel.euclidean\n\nGenerates a distance kernel based on Euclidean (straight-line) distance.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------------------------------------------|---------|\n| `ee.Kernel.euclidean(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 Euclidean distance kernel', ee.Kernel.euclidean({radius: 3}));\n\n/**\n * Output weights matrix (up to 1/1000 precision for brevity)\n *\n * [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]\n * [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]\n * [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]\n * [3.000, 2.000, 1.000, 0.000, 1.000, 2.000, 3.000]\n * [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]\n * [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]\n * [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]\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 Euclidean distance kernel:')\npprint(ee.Kernel.euclidean(**{'radius': 3}).getInfo())\n\n# Output weights matrix (up to 1/1000 precision for brevity)\n\n# [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]\n# [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]\n# [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]\n# [3.000, 2.000, 1.000, 0.000, 1.000, 2.000, 3.000]\n# [3.162, 2.236, 1.414, 1.000, 1.414, 2.236, 3.162]\n# [3.605, 2.828, 2.236, 2.000, 2.236, 2.828, 3.605]\n# [4.242, 3.605, 3.162, 3.000, 3.162, 3.605, 4.242]\n```"]]
