ee.Image.arrayCat

각 밴드에서 지정된 축을 따라 각 배열 픽셀을 연결하여 배열 이미지를 만듭니다.

사용반환 값
Image.arrayCat(image2, axis)이미지
인수유형세부정보
다음과 같은 경우: image1이미지연결할 첫 번째 배열 이미지입니다.
image2이미지연결할 두 번째 배열 이미지입니다.
axis정수연결할 축입니다.

예

코드 편집기 (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');
}

// Create two 1D array images.
var arrayImg1Da = ee.Image([0, 1, 2]).toArray();
print('1D array image (A) (pixel)', sampArrImg(arrayImg1Da));
// [0, 1, 2]
var arrayImg1Db = ee.Image([3, 4, 5]).toArray();
print('1D array image (B) (pixel)', sampArrImg(arrayImg1Db));
// [3, 4, 5]

// Concatenate 1D array image B to 1D array image A on 0-axis (rows).
var arrayImg1DcatAx0 = arrayImg1Da.arrayCat(arrayImg1Db, 0);
print('Concatenate 1D array images on 0-axis', sampArrImg(arrayImg1DcatAx0));
// [0, 1, 2, 3, 4, 5]

// Concatenate 1D array image B to 1D array image A on 1-axis (columns).
var arrayImg1DcatAx1 = arrayImg1Da.arrayCat(arrayImg1Db, 1);
print('Concatenate 1D array images on 1-axis', sampArrImg(arrayImg1DcatAx1));
// [[0, 3],
//  [1, 4]
//  [2, 5]]

// Create two 2D array images (expand the dimensions of 1D arrays).
var arrayImg2Da = arrayImg1Da.toArray(1);
print('2D array image (A) (pixel)', sampArrImg(arrayImg2Da));
// [[0],
//  [1],
//  [2]]
var arrayImg2Db = arrayImg1Db.toArray(1);
print('2D array image (B) (pixel)', sampArrImg(arrayImg2Db));
// [[3],
//  [4],
//  [5]]

// Concatenate 2D array image B to 2D array image A on 0-axis (rows).
var arrayImg2DcatAx0 = arrayImg2Da.arrayCat(arrayImg2Db, 0);
print('Concatenate 2D array images on 0-axis', sampArrImg(arrayImg2DcatAx0));
// [[0],
//  [1],
//  [2],
//  [3],
//  [4],
//  [5]]

// Concatenate 2D array image B to 2D array image A on 1-axis (columns).
var arrayImg2DcatAx1 = arrayImg2Da.arrayCat(arrayImg2Db, 1);
print('Concatenate 2D array images on 1-axis', sampArrImg(arrayImg2DcatAx1));
// [[0, 3],
//  [1, 4],
//  [2, 5]]

Python 설정

Python API 및 geemap를 사용한 대화형 개발에 관한 자세한 내용은 Python 환경 페이지를 참고하세요.

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')

# Create two 1D array images.
array_img_1d_a = ee.Image([0, 1, 2]).toArray()
print('1D array image (A) (pixel):', samp_arr_img(array_img_1d_a).getInfo())
# [0, 1, 2]
array_img_1d_b = ee.Image([3, 4, 5]).toArray()
print('1D array image (B) (pixel):', samp_arr_img(array_img_1d_b).getInfo())
# [3, 4, 5]

# Concatenate 1D array image B to 1D array image A on 0-axis (rows).
array_img_1d_cat_ax0 = array_img_1d_a.arrayCat(array_img_1d_b, 0)
print(
    'Concatenate 1D array images on 0-axis:',
    samp_arr_img(array_img_1d_cat_ax0).getInfo()
)
# [0, 1, 2, 3, 4, 5]

# Concatenate 1D array image B to 1D array image A on 1-axis (columns).
array_img_1d_cat_ax1 = array_img_1d_a.arrayCat(array_img_1d_b, 1)
print(
    'Concatenate 1D array images on 1-axis:',
    samp_arr_img(array_img_1d_cat_ax1).getInfo()
)
# [[0, 3],
#  [1, 4]
#  [2, 5]]

# Create two 2D array images (expand the dimensions of 1D arrays).
array_img_2d_a = array_img_1d_a.toArray(1)
print('2D array image (A) (pixel):', samp_arr_img(array_img_2d_a).getInfo())
# [[0],
#  [1],
#  [2]]
array_img_2d_b = array_img_1d_b.toArray(1)
print('2D array image (B) (pixel):', samp_arr_img(array_img_2d_b).getInfo())
# [[3],
#  [4],
#  [5]]

# Concatenate 2D array image B to 2D array image A on 0-axis (rows).
array_img_2d_cat_ax0 = array_img_2d_a.arrayCat(array_img_2d_b, 0)
print(
    'Concatenate 2D array images on 0-axis:',
    samp_arr_img(array_img_2d_cat_ax0).getInfo()
)
# [[0],
#  [1],
#  [2],
#  [3],
#  [4],
#  [5]]

# Concatenate 2D array image B to 2D array image A on 1-axis (columns).
array_img_2d_cat_ax1 = array_img_2d_a.arrayCat(array_img_2d_b, 1)
print(
    'Concatenate 2D array images on 1-axis:',
    samp_arr_img(array_img_2d_cat_ax1).getInfo()
)
# [[0, 3],
#  [1, 4],
#  [2, 5]]