ee.Image.arrayFlatten
Restez organisé à l'aide des collections
Enregistrez et classez les contenus selon vos préférences.
Convertit une image à bande unique de pixels multidimensionnels de forme égale en une image de pixels scalaires, avec une bande pour chaque élément du tableau.
Utilisation | Renvoie |
---|
Image.arrayFlatten(coordinateLabels, separator) | Image |
Argument | Type | Détails |
---|
ceci : image | Image | Image des pixels multidimensionnels à aplatir. |
coordinateLabels | Liste | Nom de chaque position le long de chaque axe. Par exemple, des tableaux 2x2 avec des axes signifiant "jour" et "couleur" peuvent avoir des libellés tels que [['lundi', 'mardi'], ['rouge', 'vert']], ce qui donne des noms de bandes "lundi_rouge", "lundi_vert", "mardi_rouge" et "mardi_vert". |
separator | Chaîne, valeur par défaut : "_" | Séparateur entre les libellés de tableau dans chaque nom de bande. |
Exemples
Éditeur de code (JavaScript)
// A function to print arrays for a selected pixel in the following examples.
function sampArrImg(arrImg) {
var point = ee.Geometry.Point([-121, 42]);
return arrImg.sample(point, 500).first().get('array');
}
// A 1D array image.
var arrayImg1D = ee.Image([0, 1, 2]).toArray();
print('1D array image (pixel)', sampArrImg(arrayImg1D));
// [0, 1, 2]
// Define image band names for a 1D array image with 3 rows. You are labeling
// all rows and columns using a list of lists; the 1st sub list defines labels
// for array rows and the 2nd (if applicable) defines labels for array columns.
var bandNames1D = [['row0', 'row1', 'row2']];
// Flatten the 1D array image into an image with n bands equal to all
// combinations of rows and columns. Here, we have 3 rows and 0 columns,
// so the result will be a 3-band image.
var imgFrom1Darray = arrayImg1D.arrayFlatten(bandNames1D);
print('Image from 1D array', imgFrom1Darray);
// Make a 2D array image by repeating the 1D array on 2-axis.
var arrayImg2D = arrayImg1D.arrayRepeat(1, 2);
print('2D array image (pixel)', sampArrImg(arrayImg2D));
// [[0, 0],
// [1, 1],
// [2, 2]]
// Define image band names for a 2D array image with 3 rows and 2 columns.
// Recall that you are labeling all rows and columns using a list of lists;
// The 1st sub list defines labels for array rows and the 2nd (if applicable)
// defines labels for array columns.
var bandNames2D = [['row0', 'row1', 'row2'], ['col0', 'col1']];
// Flatten the 2D array image into an image with n bands equal to all
// combinations of rows and columns. Here, we have 3 rows and 2 columns,
// so the result will be a 6-band image.
var imgFrom2Darray = arrayImg2D.arrayFlatten(bandNames2D);
print('Image from 2D array', imgFrom2Darray);
Configuration de Python
Consultez la page
Environnement Python pour en savoir plus sur l'API Python et sur l'utilisation de geemap
pour le développement interactif.
import ee
import geemap.core as geemap
Colab (Python)
# A function to print arrays for a selected pixel in the following examples.
def samp_arr_img(arr_img):
point = ee.Geometry.Point([-121, 42])
return arr_img.sample(point, 500).first().get('array')
# A 1D array image.
array_img_1d = ee.Image([0, 1, 2]).toArray()
print('1D array image (pixel):', samp_arr_img(array_img_1d).getInfo())
# [0, 1, 2]
# Define image band names for a 1D array image with 3 rows. You are labeling
# all rows and columns using a list of lists; the 1st sub list defines labels
# for array rows and the 2nd (if applicable) defines labels for array columns.
band_names_1d = [['row0', 'row1', 'row2']]
# Flatten the 1D array image into an image with n bands equal to all
# combinations of rows and columns. Here, we have 3 rows and 0 columns,
# so the result will be a 3-band image.
img_from_1d_array = array_img_1d.arrayFlatten(band_names_1d)
print('Image from 1D array:', img_from_1d_array.getInfo())
# Make a 2D array image by repeating the 1D array on 2-axis.
array_img_2d = array_img_1d.arrayRepeat(1, 2)
print('2D array image (pixel):', samp_arr_img(array_img_2d).getInfo())
# [[0, 0],
# [1, 1],
# [2, 2]]
# Define image band names for a 2D array image with 3 rows and 2 columns.
# Recall that you are labeling all rows and columns using a list of lists;
# The 1st sub list defines labels for array rows and the 2nd (if applicable)
# defines labels for array columns.
band_names_2d = [['row0', 'row1', 'row2'], ['col0', 'col1']]
# Flatten the 2D array image into an image with n bands equal to all
# combinations of rows and columns. Here, we have 3 rows and 2 columns,
# so the result will be a 6-band image.
img_from_2d_array = array_img_2d.arrayFlatten(band_names_2d)
print('Image from 2D array:', img_from_2d_array.getInfo())
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
Dernière mise à jour le 2025/07/26 (UTC).
[null,null,["Dernière mise à jour le 2025/07/26 (UTC)."],[[["\u003cp\u003e\u003ccode\u003eImage.arrayFlatten\u003c/code\u003e transforms an image containing multidimensional pixel arrays into a multi-band image with scalar pixel values.\u003c/p\u003e\n"],["\u003cp\u003eEach element of the input array becomes a separate band in the output image.\u003c/p\u003e\n"],["\u003cp\u003eUsers can specify custom names for the output bands using the \u003ccode\u003ecoordinateLabels\u003c/code\u003e parameter.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eseparator\u003c/code\u003e parameter allows for customization of the delimiter used in band names derived from array indices.\u003c/p\u003e\n"],["\u003cp\u003eThis function is useful for working with data structured as arrays within an image, such as time series or multi-spectral data organized in matrices.\u003c/p\u003e\n"]]],[],null,["# ee.Image.arrayFlatten\n\nConverts a single-band image of equal-shape multidimensional pixels to an image of scalar pixels, with one band for each element of the array.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|-------------------------------------------------------|---------|\n| Image.arrayFlatten`(coordinateLabels, `*separator*`)` | Image |\n\n| Argument | Type | Details |\n|--------------------|----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| this: `image` | Image | Image of multidimensional pixels to flatten. |\n| `coordinateLabels` | List | Name of each position along each axis. For example, 2x2 arrays with axes meaning 'day' and 'color' could have labels like \\[\\['monday', 'tuesday'\\], \\['red', 'green'\\]\\], resulting in band names'monday_red', 'monday_green', 'tuesday_red', and 'tuesday_green'. |\n| `separator` | String, default: \"_\" | Separator between array labels in each band name. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// A function to print arrays for a selected pixel in the following examples.\nfunction sampArrImg(arrImg) {\n var point = ee.Geometry.Point([-121, 42]);\n return arrImg.sample(point, 500).first().get('array');\n}\n\n// A 1D array image.\nvar arrayImg1D = ee.Image([0, 1, 2]).toArray();\nprint('1D array image (pixel)', sampArrImg(arrayImg1D));\n// [0, 1, 2]\n\n// Define image band names for a 1D array image with 3 rows. You are labeling\n// all rows and columns using a list of lists; the 1st sub list defines labels\n// for array rows and the 2nd (if applicable) defines labels for array columns.\nvar bandNames1D = [['row0', 'row1', 'row2']];\n\n// Flatten the 1D array image into an image with n bands equal to all\n// combinations of rows and columns. Here, we have 3 rows and 0 columns,\n// so the result will be a 3-band image.\nvar imgFrom1Darray = arrayImg1D.arrayFlatten(bandNames1D);\nprint('Image from 1D array', imgFrom1Darray);\n\n// Make a 2D array image by repeating the 1D array on 2-axis.\nvar arrayImg2D = arrayImg1D.arrayRepeat(1, 2);\nprint('2D array image (pixel)', sampArrImg(arrayImg2D));\n// [[0, 0],\n// [1, 1],\n// [2, 2]]\n\n// Define image band names for a 2D array image with 3 rows and 2 columns.\n// Recall that you are labeling all rows and columns using a list of lists;\n// The 1st sub list defines labels for array rows and the 2nd (if applicable)\n// defines labels for array columns.\nvar bandNames2D = [['row0', 'row1', 'row2'], ['col0', 'col1']];\n\n// Flatten the 2D array image into an image with n bands equal to all\n// combinations of rows and columns. Here, we have 3 rows and 2 columns,\n// so the result will be a 6-band image.\nvar imgFrom2Darray = arrayImg2D.arrayFlatten(bandNames2D);\nprint('Image from 2D array', imgFrom2Darray);\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\n# A function to print arrays for a selected pixel in the following examples.\ndef samp_arr_img(arr_img):\n point = ee.Geometry.Point([-121, 42])\n return arr_img.sample(point, 500).first().get('array')\n\n# A 1D array image.\narray_img_1d = ee.Image([0, 1, 2]).toArray()\nprint('1D array image (pixel):', samp_arr_img(array_img_1d).getInfo())\n# [0, 1, 2]\n\n# Define image band names for a 1D array image with 3 rows. You are labeling\n# all rows and columns using a list of lists; the 1st sub list defines labels\n# for array rows and the 2nd (if applicable) defines labels for array columns.\nband_names_1d = [['row0', 'row1', 'row2']]\n\n# Flatten the 1D array image into an image with n bands equal to all\n# combinations of rows and columns. Here, we have 3 rows and 0 columns,\n# so the result will be a 3-band image.\nimg_from_1d_array = array_img_1d.arrayFlatten(band_names_1d)\nprint('Image from 1D array:', img_from_1d_array.getInfo())\n\n# Make a 2D array image by repeating the 1D array on 2-axis.\narray_img_2d = array_img_1d.arrayRepeat(1, 2)\nprint('2D array image (pixel):', samp_arr_img(array_img_2d).getInfo())\n# [[0, 0],\n# [1, 1],\n# [2, 2]]\n\n# Define image band names for a 2D array image with 3 rows and 2 columns.\n# Recall that you are labeling all rows and columns using a list of lists;\n# The 1st sub list defines labels for array rows and the 2nd (if applicable)\n# defines labels for array columns.\nband_names_2d = [['row0', 'row1', 'row2'], ['col0', 'col1']]\n\n# Flatten the 2D array image into an image with n bands equal to all\n# combinations of rows and columns. Here, we have 3 rows and 2 columns,\n# so the result will be a 6-band image.\nimg_from_2d_array = array_img_2d.arrayFlatten(band_names_2d)\nprint('Image from 2D array:', img_from_2d_array.getInfo())\n```"]]