ee.ImageCollection.getRegion

ImageCollection의 각 [픽셀, 밴드, 이미지] 튜플에 대한 값 배열을 출력합니다. 출력에는 지정된 영역의 각 픽셀과 교차하는 각 이미지의 id, lon, lat, time, 모든 밴드 행이 포함됩니다. 1048576개를 초과하는 값을 추출하려고 하면 오류가 발생합니다.

사용	반환 값
ImageCollection.getRegion(geometry, scale, crs, crsTransform)	목록

인수	유형	세부정보
collection	ImageCollection	데이터를 추출할 이미지 컬렉션입니다.
geometry	도형	데이터를 추출할 리전입니다.
scale	부동 소수점 수, 기본값: null	작업할 투영의 명목상 척도(미터)입니다.
crs	투영(선택사항)	작업할 투영입니다. 지정하지 않으면 기본값은 EPSG:4326입니다. 크기 조정 외에 지정된 경우 프로젝션이 지정된 크기로 다시 조정됩니다.
crsTransform	목록, 기본값: null	CRS 변환 값의 배열입니다. 이는 3x2 어파인 변환의 행 우선 순서입니다. 이 옵션은 scale 옵션과 상호 배타적이며 지정된 프로젝션에 이미 설정된 변환을 대체합니다.

예

코드 편집기(JavaScript)

// A Landsat 8 TOA image collection (3 months at a specific point, RGB bands).
var col = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
  .filterBounds(ee.Geometry.Point(-90.70, 34.71))
  .filterDate('2020-07-01', '2020-10-01')
  .select('B[2-4]');
print('Collection', col);

// Define a region to get pixel values for. This is a small rectangle region
// that intersects 2 image pixels at 30-meter scale.
var roi = ee.Geometry.BBox(-90.496353, 34.851971, -90.495749, 34.852197);

// Display the region of interest overlaid on an image representative. Note
// the ROI intersection with 2 pixels.
var visParams = {
  bands: ['B4', 'B3', 'B2'],
  min: 0.128,
  max: 0.163
};
Map.setCenter(-90.49605, 34.85211, 19);
Map.addLayer(col.first(), visParams, 'Image representative');
Map.addLayer(roi, {color: 'white'}, 'ROI');

// Fetch pixel-level information from all images in the collection for the
// pixels intersecting the ROI.
var pixelInfoBbox = col.getRegion({
  geometry: roi,
  scale: 30
});

// The result is a table (a list of lists) where the first row is column
// labels and subsequent rows are image pixels. Columns contain values for
// the image ID ('system:index'), pixel longitude and latitude, image
// observation time ('system:time_start'), and bands. In this example, note
// that there are 5 images and the region intersects 2 pixels, so n rows
// equals 11 (5 * 2 + 1). All collection images must have the same number of
// bands with the same names.
print('Extracted pixel info', pixelInfoBbox);

// The function accepts all geometry types (e.g., points, lines, polygons).
// Here, a multi-point geometry with two points is used.
var points = ee.Geometry.MultiPoint([[-90.49, 34.85], [-90.48, 34.84]]);
var pixelInfoPoints = col.getRegion({
  geometry: points,
  scale: 30
});
print('Point geometry example', pixelInfoPoints);

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 Landsat 8 TOA image collection (3 months at a specific point, RGB bands).
col = (
    ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
    .filterBounds(ee.Geometry.Point(-90.70, 34.71))
    .filterDate('2020-07-01', '2020-10-01')
    .select('B[2-4]')
)
display('Collection', col)

# Define a region to get pixel values for. This is a small rectangle region
# that intersects 2 image pixels at 30-meter scale.
roi = ee.Geometry.BBox(-90.496353, 34.851971, -90.495749, 34.852197)

# Display the region of interest overlaid on an image representative. Note
# the ROI intersection with 2 pixels.
vis_params = {'bands': ['B4', 'B3', 'B2'], 'min': 0.128, 'max': 0.163}
m = geemap.Map()
m.set_center(-90.49605, 34.85211, 19)
m.add_layer(col.first(), vis_params, 'Image representative')
m.add_layer(roi, {'color': 'white'}, 'ROI')
display(m)

# Fetch pixel-level information from all images in the collection for the
# pixels intersecting the ROI.
pixel_info_bbox = col.getRegion(geometry=roi, scale=30)

# The result is a table (a list of lists) where the first row is column
# labels and subsequent rows are image pixels. Columns contain values for
# the image ID ('system:index'), pixel longitude and latitude, image
# observation time ('system:time_start'), and bands. In this example, note
# that there are 5 images and the region intersects 2 pixels, so n rows
# equals 11 (5 * 2 + 1). All collection images must have the same number of
# bands with the same names.
display('Extracted pixel info', pixel_info_bbox)

# The function accepts all geometry types (e.g., points, lines, polygons).
# Here, a multi-point geometry with two points is used.
points = ee.Geometry.MultiPoint([[-90.49, 34.85], [-90.48, 34.84]])
pixel_info_points = col.getRegion(geometry=points, scale=30)
display('Point geometry example', pixel_info_points)