ee.Image.sample

對圖片的像素取樣，並以 FeatureCollection 形式傳回。輸入圖片中的每個波段都會有 1 個屬性。請注意，預設行為是捨棄與遮罩像素相交的特徵，這會導致屬性值為空值 (請參閱 dropNulls 引數)。

用量	傳回
Image.sample(region, scale, projection, factor, numPixels, seed, dropNulls, tileScale, geometries)	FeatureCollection

引數	類型	詳細資料
這個：image	圖片	要取樣的圖片。
region	幾何圖形，預設值：null	要取樣的區域。如未指定，則會使用圖片的完整足跡。
scale	浮點值，預設值為空值	投影的公尺名義比例，用於取樣。
projection	投影，預設值：null	要取樣的投影。如未指定，系統會使用圖片第一個波段的投影。如果除了比例外還指定了其他值，系統會重新調整為指定比例。
factor	浮點值，預設值為空值	向下取樣係數，介於 (0, 1] 之間。如果指定了「numPixels」，就不得指定「numPixels」。預設值為不進行子取樣。
numPixels	Long，預設值：null	要取樣的像素大致數量。如要指定「factor」，就不得指定「factor」。
seed	整數，預設值為 0	用於子取樣的隨機化種子。
dropNulls	布林值，預設值為 true	在篩選結果後，捨棄具有空值屬性的特徵。
tileScale	浮點值，預設值為 1	用於縮小聚合圖塊大小的縮放比例因數；使用較大的 tileScale (例如 2 或 4) 可能會啟用運算，導致記憶體不足。
geometries	布林值，預設值為 false	如果為 true，則會將取樣像素的中心新增為輸出特徵的幾何屬性。否則系統會省略幾何圖形 (節省記憶體)。

範例

程式碼編輯器 (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 設定

請參閱 Python 環境頁面，瞭解 Python API 和如何使用 geemap 進行互動式開發。

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())