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ee.Kernel.manhattan
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
根據直線 (城市街區) 距離產生距離核心。
| 用量 | 傳回 |
|---|
ee.Kernel.manhattan(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 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 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 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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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 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```"]]
