ee.Kernel.add
קל לארגן דפים בעזרת אוספים
אפשר לשמור ולסווג תוכן על סמך ההעדפות שלך.
הפעולה מוסיפה שני גרעינים (pointwise) אחרי יישור המרכזים שלהם.
| שימוש | החזרות |
|---|
Kernel.add(kernel2, normalize) | ליבה |
| ארגומנט | סוג | פרטים |
|---|
זה: kernel1 | ליבה | הליבה הראשונה. |
kernel2 | ליבה | הליבה השנייה. |
normalize | בוליאני, ברירת מחדל: false | מנרמלים את הליבה. |
דוגמאות
עורך הקוד (JavaScript)
// Two kernels, they do not need to have the same dimensions.
var kernelA = ee.Kernel.chebyshev({radius: 3});
var kernelB = ee.Kernel.square({radius: 1, normalize: false, magnitude: 100});
print(kernelA, kernelB);
/**
* Two kernel weights matrices
*
* [3, 3, 3, 3, 3, 3, 3]
* [3, 2, 2, 2, 2, 2, 3]
* [3, 2, 1, 1, 1, 2, 3] [100, 100, 100]
* A [3, 2, 1, 0, 1, 2, 3] B [100, 100, 100]
* [3, 2, 1, 1, 1, 2, 3] [100, 100, 100]
* [3, 2, 2, 2, 2, 2, 3]
* [3, 3, 3, 3, 3, 3, 3]
*/
print('Pointwise addition of two kernels', kernelA.add(kernelB));
/**
* [3, 3, 3, 3, 3, 3, 3]
* [3, 2, 2, 2, 2, 2, 3]
* [3, 2, 101, 101, 101, 2, 3]
* [3, 2, 101, 100, 101, 2, 3]
* [3, 2, 101, 101, 101, 2, 3]
* [3, 2, 2, 2, 2, 2, 3]
* [3, 3, 3, 3, 3, 3, 3]
*/
הגדרת Python
מידע על Python API ועל שימוש ב-geemap לפיתוח אינטראקטיבי מופיע בדף
Python Environment.
import ee
import geemap.core as geemap
Colab (Python)
from pprint import pprint
# Two kernels, they do not need to have the same dimensions.
kernel_a = ee.Kernel.chebyshev(**{'radius': ee.Number(3)})
kernel_b = ee.Kernel.square(**{
'radius': 1,
'normalize': False,
'magnitude': 100
})
pprint(kernel_a.getInfo())
pprint(kernel_b.getInfo())
# Two kernel weights matrices
# [3, 3, 3, 3, 3, 3, 3]
# [3, 2, 2, 2, 2, 2, 3]
# [3, 2, 1, 1, 1, 2, 3] [100, 100, 100]
# A [3, 2, 1, 0, 1, 2, 3] B [100, 100, 100]
# [3, 2, 1, 1, 1, 2, 3] [100, 100, 100]
# [3, 2, 2, 2, 2, 2, 3]
# [3, 3, 3, 3, 3, 3, 3]
print('Pointwise addition of two kernels:')
pprint(kernel_a.add(kernel_b).getInfo())
# [3, 3, 3, 3, 3, 3, 3]
# [3, 2, 2, 2, 2, 2, 3]
# [3, 2, 101, 101, 101, 2, 3]
# [3, 2, 101, 100, 101, 2, 3]
# [3, 2, 101, 101, 101, 2, 3]
# [3, 2, 2, 2, 2, 2, 3]
# [3, 3, 3, 3, 3, 3, 3]
אלא אם צוין אחרת, התוכן של דף זה הוא ברישיון Creative Commons Attribution 4.0 ודוגמאות הקוד הן ברישיון Apache 2.0. לפרטים, ניתן לעיין במדיניות האתר Google Developers. Java הוא סימן מסחרי רשום של חברת Oracle ו/או של השותפים העצמאיים שלה.
עדכון אחרון: 2025-07-27 (שעון UTC).
[null,null,["עדכון אחרון: 2025-07-27 (שעון UTC)."],[[["\u003cp\u003e\u003ccode\u003eKernel.add\u003c/code\u003e combines two kernels by adding their weights pointwise after aligning their centers.\u003c/p\u003e\n"],["\u003cp\u003eThe resulting kernel has the same dimensions as the larger of the two input kernels.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003enormalize\u003c/code\u003e parameter can be used to normalize the resulting kernel.\u003c/p\u003e\n"],["\u003cp\u003eKernels do not need to have the same dimensions to be added.\u003c/p\u003e\n"]]],[],null,["# ee.Kernel.add\n\nAdds two kernels (pointwise) after aligning their centers.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------|---------|\n| Kernel.add`(kernel2, `*normalize*`)` | Kernel |\n\n| Argument | Type | Details |\n|-----------------|-------------------------|-----------------------|\n| this: `kernel1` | Kernel | The first kernel. |\n| `kernel2` | Kernel | The second kernel. |\n| `normalize` | Boolean, default: false | Normalize the kernel. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// Two kernels, they do not need to have the same dimensions.\nvar kernelA = ee.Kernel.chebyshev({radius: 3});\nvar kernelB = ee.Kernel.square({radius: 1, normalize: false, magnitude: 100});\nprint(kernelA, kernelB);\n\n/**\n * Two kernel weights matrices\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] [100, 100, 100]\n * A [3, 2, 1, 0, 1, 2, 3] B [100, 100, 100]\n * [3, 2, 1, 1, 1, 2, 3] [100, 100, 100]\n * [3, 2, 2, 2, 2, 2, 3]\n * [3, 3, 3, 3, 3, 3, 3]\n */\n\nprint('Pointwise addition of two kernels', kernelA.add(kernelB));\n\n/**\n * [3, 3, 3, 3, 3, 3, 3]\n * [3, 2, 2, 2, 2, 2, 3]\n * [3, 2, 101, 101, 101, 2, 3]\n * [3, 2, 101, 100, 101, 2, 3]\n * [3, 2, 101, 101, 101, 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\n# Two kernels, they do not need to have the same dimensions.\nkernel_a = ee.Kernel.chebyshev(**{'radius': ee.Number(3)})\nkernel_b = ee.Kernel.square(**{\n 'radius': 1,\n 'normalize': False,\n 'magnitude': 100\n})\npprint(kernel_a.getInfo())\npprint(kernel_b.getInfo())\n\n# Two kernel weights matrices\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] [100, 100, 100]\n# A [3, 2, 1, 0, 1, 2, 3] B [100, 100, 100]\n# [3, 2, 1, 1, 1, 2, 3] [100, 100, 100]\n# [3, 2, 2, 2, 2, 2, 3]\n# [3, 3, 3, 3, 3, 3, 3]\n\nprint('Pointwise addition of two kernels:')\npprint(kernel_a.add(kernel_b).getInfo())\n\n# [3, 3, 3, 3, 3, 3, 3]\n# [3, 2, 2, 2, 2, 2, 3]\n# [3, 2, 101, 101, 101, 2, 3]\n# [3, 2, 101, 100, 101, 2, 3]\n# [3, 2, 101, 101, 101, 2, 3]\n# [3, 2, 2, 2, 2, 2, 3]\n# [3, 3, 3, 3, 3, 3, 3]\n```"]]
