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ee.Kernel.gaussian
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
从抽样的连续高斯生成高斯核。
用法 | 返回 |
---|
ee.Kernel.gaussian(radius, sigma, units, normalize, magnitude) | 内核 |
参数 | 类型 | 详细信息 |
---|
radius | 浮点数 | 要生成的内核的半径。 |
sigma | 浮点数,默认值:1 | 高斯函数的标准差(与半径的单位相同)。 |
units | 字符串,默认值:“pixels” | 内核的测量系统(“像素”或“米”)。如果以米为单位指定了内核,则当缩放级别发生变化时,内核会调整大小。 |
normalize | 布尔值,默认值:true | 将内核值归一化为总和为 1。 |
magnitude | 浮点数,默认值:1 | 按此量缩放每个值。 |
示例
代码编辑器 (JavaScript)
print('A Gaussian kernel', ee.Kernel.gaussian({radius: 3}));
/**
* Output weights matrix (up to 1/1000 precision for brevity)
*
* [0.002, 0.013, 0.021, 0.013, 0.002]
* [0.013, 0.059, 0.098, 0.059, 0.013]
* [0.021, 0.098, 0.162, 0.098, 0.021]
* [0.013, 0.059, 0.098, 0.059, 0.013]
* [0.002, 0.013, 0.021, 0.013, 0.002]
*/
Python 设置
如需了解 Python API 和如何使用 geemap
进行交互式开发,请参阅
Python 环境页面。
import ee
import geemap.core as geemap
Colab (Python)
from pprint import pprint
print('A Gaussian kernel:')
pprint(ee.Kernel.gaussian(**{'radius': 3}).getInfo())
# Output weights matrix (up to 1/1000 precision for brevity)
# [0.002, 0.013, 0.021, 0.013, 0.002]
# [0.013, 0.059, 0.098, 0.059, 0.013]
# [0.021, 0.098, 0.162, 0.098, 0.021]
# [0.013, 0.059, 0.098, 0.059, 0.013]
# [0.002, 0.013, 0.021, 0.013, 0.002]
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-07-29。
[null,null,["最后更新时间 (UTC):2025-07-29。"],[[["\u003cp\u003eThe \u003ccode\u003eee.Kernel.gaussian\u003c/code\u003e function generates a Gaussian kernel, which is essentially a matrix of weights used for image processing, derived from a continuous Gaussian distribution.\u003c/p\u003e\n"],["\u003cp\u003eUsers can customize the kernel by defining its radius, standard deviation (\u003ccode\u003esigma\u003c/code\u003e), units (pixels or meters), normalization, and magnitude (scaling factor).\u003c/p\u003e\n"],["\u003cp\u003eBy default, the kernel is normalized, meaning the sum of its values equals 1, and has a magnitude of 1, applying no scaling to the pixel values.\u003c/p\u003e\n"],["\u003cp\u003eThe generated Gaussian kernel can be applied to imagery to perform various operations such as blurring or smoothing, as demonstrated in the example code snippets.\u003c/p\u003e\n"]]],["The core function is to generate a Gaussian kernel using `ee.Kernel.gaussian()`. This function requires a `radius` and accepts optional parameters like `sigma` (standard deviation), `units` ('pixels' or 'meters'), `normalize` (kernel value normalization), and `magnitude` (scaling factor). The output is a kernel object. Example code demonstrates how to create and print a Gaussian kernel in JavaScript and Python, including the resulting weights matrix.\n"],null,["# ee.Kernel.gaussian\n\nGenerates a Gaussian kernel from a sampled continuous Gaussian.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|-------------------------------------------------------------------------------------|---------|\n| `ee.Kernel.gaussian(radius, `*sigma* `, `*units* `, `*normalize* `, `*magnitude*`)` | Kernel |\n\n| Argument | Type | Details |\n|-------------|---------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `radius` | Float | The radius of the kernel to generate. |\n| `sigma` | Float, default: 1 | Standard deviation of the Gaussian function (same units as radius). |\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: true | 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 Gaussian kernel', ee.Kernel.gaussian({radius: 3}));\n\n/**\n * Output weights matrix (up to 1/1000 precision for brevity)\n *\n * [0.002, 0.013, 0.021, 0.013, 0.002]\n * [0.013, 0.059, 0.098, 0.059, 0.013]\n * [0.021, 0.098, 0.162, 0.098, 0.021]\n * [0.013, 0.059, 0.098, 0.059, 0.013]\n * [0.002, 0.013, 0.021, 0.013, 0.002]\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 Gaussian kernel:')\npprint(ee.Kernel.gaussian(**{'radius': 3}).getInfo())\n\n# Output weights matrix (up to 1/1000 precision for brevity)\n\n# [0.002, 0.013, 0.021, 0.013, 0.002]\n# [0.013, 0.059, 0.098, 0.059, 0.013]\n# [0.021, 0.098, 0.162, 0.098, 0.021]\n# [0.013, 0.059, 0.098, 0.059, 0.013]\n# [0.002, 0.013, 0.021, 0.013, 0.002]\n```"]]