ee.Image.sample

对图片的像素进行采样，并以 FeatureCollection 形式返回。每个特征在输入图片中每个波段都将有 1 个属性。请注意，默认行为是舍弃与遮盖像素相交的特征，这会导致属性值为 null（请参阅 dropNulls 实参）。

用法返回
Image.sample(region, scale, projection, factor, numPixels, seed, dropNulls, tileScale, geometries)FeatureCollection
参数类型详细信息
此：image图片要抽样的图片。
region几何图形，默认值：null要从中抽样的区域。如果未指定，则使用映像的整个占用空间。
scale浮点数，默认值：null要采样的投影的标称比例（以米为单位）。
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。
factor浮点数，默认值：null一个介于 (0, 1] 范围内的子采样率。如果指定了此参数，则不得指定“numPixels”。默认值为不进行子采样。
numPixelsLong，默认值：null要采样的像素的大致数量。如果指定了此字段，则不得指定“factor”。
seed整数，默认值：0用于子采样的随机化种子。
dropNulls布尔值，默认值：true对结果进行后过滤，以舍弃具有 null 值属性的特征。
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，则将采样像素的中心添加为输出要素的几何属性。否则，系统会省略几何图形（节省内存）。

示例

代码编辑器 (JavaScript)

// Demonstrate extracting pixels from an image as features with
// ee.Image.sample(), and show how the features are aligned with the pixels.

// An image with one band of elevation data.
var image = ee.Image('CGIAR/SRTM90_V4');
var VIS_MIN = 1620;
var VIS_MAX = 1650;
Map.addLayer(image, {min: VIS_MIN, max: VIS_MAX}, 'SRTM');

// Region to sample.
var region = ee.Geometry.Polygon(
  [[[-110.006, 40.002],
    [-110.006, 39.999],
    [-109.995, 39.999],
    [-109.995, 40.002]]], null, false);
// Show region on the map.
Map.setCenter(-110, 40, 16);
Map.addLayer(ee.FeatureCollection([region]).style({"color": "00FF0022"}));

// Perform sampling; convert image pixels to features.
var samples = image.sample({
  region: region,

  // Default (false) is no geometries in the output.
  // When set to true, each feature has a Point geometry at the center of the
  // image pixel.
  geometries: true,

  // The scale is not specified, so the resolution of the image will be used,
  // and there is a feature for every pixel. If we give a scale parameter, the
  // image will be resampled and there will be more or fewer features.
  //
  // scale: 200,
});

// Visualize sample data using ee.FeatureCollection.style().
var styled = samples
  .map(function (feature) {
    return feature.set('style', {
      pointSize: feature.getNumber('elevation').unitScale(VIS_MIN, VIS_MAX)
          .multiply(15),
    });
  })
  .style({
    color: '000000FF',
    fillColor: '00000000',
    styleProperty: 'style',
    neighborhood: 6,  // increase to correctly draw large points
  });
Map.addLayer(styled);

// Each sample feature has a point geometry and a property named 'elevation'
// corresponding to the band named 'elevation' of the image. If there are
// multiple bands they will become multiple properties. This will print:
//
// geometry: Point (-110.01, 40.00)
// properties:
//   elevation: 1639
print(samples.first());

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)

# Demonstrate extracting pixels from an image as features with
# ee.Image.sample(), and show how the features are aligned with the pixels.

# An image with one band of elevation data.
image = ee.Image('CGIAR/SRTM90_V4')
vis_min = 1620
vis_max = 1650
m = geemap.Map()
m.add_layer(image, {'min': vis_min, 'max': vis_max}, 'SRTM')

# Region to sample.
region = ee.Geometry.Polygon(
    [[
        [-110.006, 40.002],
        [-110.006, 39.999],
        [-109.995, 39.999],
        [-109.995, 40.002],
    ]],
    None,
    False,
)
# Show region on the map.
m.set_center(-110, 40, 16)

m.add_layer(ee.FeatureCollection([region]).style(color='00FF0022'))

# Perform sampling convert image pixels to features.
samples = image.sample(
    region=region,
    # Default (False) is no geometries in the output.
    # When set to True, each feature has a Point geometry at the center of the
    # image pixel.
    geometries=True,
    # The scale is not specified, so the resolution of the image will be used,
    # and there is a feature for every pixel. If we give a scale parameter, the
    # image will be resampled and there will be more or fewer features.
    #
    # scale=200,
)


def scale_point_size(feature):
  elevation = feature.getNumber('elevation')
  point_size = elevation.unitScale(vis_min, vis_max).multiply(15)
  feature.set('style', {'pointSize': point_size})
  return feature


# Visualize sample data using ee.FeatureCollection.style().
styled = samples.map(scale_point_size).style(
    color='000000FF',
    fillColor='00000000',
    styleProperty='style',
    neighborhood=6,  # increase to correctly draw large points
)
m.add_layer(styled)
display(m)

# Each sample feature has a point geometry and a property named 'elevation'
# corresponding to the band named 'elevation' of the image. If there are
# multiple bands they will become multiple properties. This will print:
#
# geometry: Point (-110.01, 40.00)
# properties:
#   elevation: 1639
display(samples.first())