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ee.Kernel.euclidean
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
根據歐幾里得 (直線) 距離產生距離核心。
| 用量 | 傳回 |
|---|
ee.Kernel.euclidean(radius, units, normalize, magnitude) | 核心 |
| 引數 | 類型 | 詳細資料 |
|---|
radius | 浮點值 | 要產生的核心半徑。 |
units | 字串，預設值為「pixels」 | 核心的測量系統 (「像素」或「公尺」)。如果核心是以公尺為單位指定，則會在變更縮放層級時調整大小。 |
normalize | 布林值，預設值為 false | 將核心值正規化為總和為 1。 |
magnitude | 浮點值，預設值為 1 | 將每個值按此金額縮放。 |
範例
程式碼編輯器 (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]
*/
Python 設定
請參閱
Python 環境頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。
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]
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上次更新時間：2025-07-26 (世界標準時間)。
[null,null,["上次更新時間：2025-07-26 (世界標準時間)。"],[[["\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```"]]
