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此页面由 Cloud Translation API 翻译。

ee.Image.sampleRegions

将与一个或多个区域相交的图片（按给定比例）的每个像素转换为要素，并以 FeatureCollection 的形式返回这些要素。每个输出要素都将具有输入图片的每个波段对应的属性，以及从输入要素复制的任何指定属性。

请注意，几何图形将贴靠到像素中心。

用法	返回
`Image.sampleRegions(collection, properties, scale, projection, tileScale, geometries)`	FeatureCollection

参数	类型	详细信息
此：`image`	图片	要抽样的图片。
`collection`	FeatureCollection	要进行抽样的区域。
`properties`	列表，默认值：null	要从每个输入特征复制的属性列表。默认为所有非系统属性。
`scale`	浮点数，默认值：null	要采样的投影的标称比例（以米为单位）。如果未指定，则使用映像第一个波段的缩放比例。
`projection`	投影，默认值：null	要进行抽样的投影。如果未指定，则使用映像第一个波段的投影。如果除了缩放比例之外还指定了此参数，则会重新缩放到指定的缩放比例。
`tileScale`	浮点数，默认值：1	用于减小聚合图块大小的缩放比例；使用较大的 tileScale（例如，2 或 4）可能会启用内存不足的计算（使用默认值）。
`geometries`	布尔值，默认值：false	如果为 true，则结果将包含每个抽样像素的点几何图形。否则，系统会省略几何图形（节省内存）。

示例

代码编辑器 (JavaScript)

// A Sentinel-2 surface reflectance image.
var img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG');
Map.setCenter(-122.503881, 37.765588, 18);
Map.addLayer(img, {bands: ['B11', 'B8', 'B3'], min: 100, max: 4500}, 'img');

// A feature collection with two polygon regions each intersecting 36
// pixels at 10 m scale.
var fcPolygon = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Rectangle(
    -122.50620929, 37.76502806, -122.50552264, 37.76556663), {id: 0}),
  ee.Feature(ee.Geometry.Rectangle(
    -122.50530270, 37.76565568, -122.50460533, 37.76619425), {id: 1})
]);
Map.addLayer(fcPolygon, {color: 'yellow'}, 'fcPolygon');

var fcPolygonSamp = img.sampleRegions({
  collection: fcPolygon,
  scale: 10,
  geometries: true
});
// Note that 7 pixels are missing from the sample. If a pixel contains a masked
// band value it will be excluded from the sample. In this case, the TCI_B band
// is masked for each unsampled pixel.
print('A feature per pixel (at given scale) in each region', fcPolygonSamp);
Map.addLayer(fcPolygonSamp, {color: 'purple'}, 'fcPolygonSamp');

// A feature collection with two points intersecting two different pixels.
// This example is included to show the behavior for point geometries. In
// practice, if the feature collection is all points, ee.Image.reduceRegions
// should be used instead to save memory.
var fcPoint = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Point([-122.50309256, 37.76605006]), {id: 0}),
  ee.Feature(ee.Geometry.Point([-122.50344661, 37.76560903]), {id: 1})
]);
Map.addLayer(fcPoint, {color: 'cyan'}, 'fcPoint');

var fcPointSamp = img.sampleRegions({
  collection: fcPoint,
  scale: 10
});
print('A feature per point', fcPointSamp);

Python 设置

如需了解 Python API 和如何使用 geemap 进行交互式开发，请参阅 Python 环境页面。

import ee
import geemap.core as geemap

Colab (Python)

# A Sentinel-2 surface reflectance image.
img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')
m = geemap.Map()
m.set_center(-122.503881, 37.765588, 18)
m.add_layer(
    img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 4500}, 'img'
)
display(m)

# A feature collection with two polygon regions each intersecting 36
# pixels at 10 m scale.
fc_polygon = ee.FeatureCollection([
    ee.Feature(
        ee.Geometry.Rectangle(
            -122.50620929, 37.76502806, -122.50552264, 37.76556663
        ),
        {'id': 0},
    ),
    ee.Feature(
        ee.Geometry.Rectangle(
            -122.50530270, 37.76565568, -122.50460533, 37.76619425
        ),
        {'id': 1},
    ),
])
m.add_layer(fc_polygon, {'color': 'yellow'}, 'fc_polygon')

fc_polygon_samp = img.sampleRegions(
    collection=fc_polygon, scale=10, geometries=True
)
# Note that 7 pixels are missing from the sample. If a pixel contains a masked
# band value it will be excluded from the sample. In this case, the TCI_B band
# is masked for each unsampled pixel.
display('A feature per pixel (at given scale) in each region', fc_polygon_samp)
m.add_layer(fc_polygon_samp, {'color': 'purple'}, 'fc_polygon_samp')

# A feature collection with two points intersecting two different pixels.
# This example is included to show the behavior for point geometries. In
# practice, if the feature collection is all points, ee.Image.reduceRegions
# should be used instead to save memory.
fc_point = ee.FeatureCollection([
    ee.Feature(ee.Geometry.Point([-122.50309256, 37.76605006]), {'id': 0}),
    ee.Feature(ee.Geometry.Point([-122.50344661, 37.76560903]), {'id': 1}),
])
m.add_layer(fc_point, {'color': 'cyan'}, 'fc_point')

fc_point_samp = img.sampleRegions(collection=fc_point, scale=10)
display('A feature per point', fc_point_samp)

ee.Image.sampleRegions 使用集合让一切井井有条 根据您的偏好保存内容并对其进行分类。

示例

代码编辑器 (JavaScript)

Colab (Python)

ee.Image.sampleRegions