ee.Image.sample

Mengambil sampel piksel gambar, lalu menampilkannya sebagai FeatureCollection. Setiap fitur akan memiliki 1 properti per band dalam gambar input. Perhatikan bahwa perilaku defaultnya adalah menghilangkan fitur yang berpotongan dengan piksel yang di-masking, yang menghasilkan properti bernilai null (lihat argumen dropNulls).

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

Argumen	Jenis	Detail
ini: image	Gambar	Gambar yang akan diambil sampelnya.
region	Geometri, default: null	Region tempat sampel diambil. Jika tidak ditentukan, akan menggunakan seluruh jejak gambar.
scale	Float, default: null	Skala nominal dalam meter proyeksi yang akan diambil sampelnya.
projection	Proyeksi, default: null	Proyeksi tempat pengambilan sampel. Jika tidak ditentukan, proyeksi band pertama gambar akan digunakan. Jika ditentukan selain skala, akan diubah skalanya ke skala yang ditentukan.
factor	Float, default: null	Faktor subsampling, dalam (0, 1]. Jika ditentukan, 'numPixels' tidak boleh ditentukan. Defaultnya adalah tanpa subsampling.
numPixels	Long, default: null	Perkiraan jumlah piksel yang akan diambil sampelnya. Jika ditentukan, 'factor' tidak boleh ditentukan.
seed	Bilangan bulat, default: 0	Bibit pengacakan yang akan digunakan untuk pengambilan sampel sekunder.
dropNulls	Boolean, default: benar (true)	Memfilter hasil setelahnya untuk menghilangkan fitur yang memiliki properti bernilai null.
tileScale	Float, default: 1	Faktor penskalaan yang digunakan untuk mengurangi ukuran petak agregasi; menggunakan tileScale yang lebih besar (misalnya, 2 atau 4) dapat mengaktifkan komputasi yang kehabisan memori dengan nilai default.
geometries	Boolean, default: false	Jika benar, menambahkan titik tengah piksel yang diambil sampelnya sebagai properti geometri fitur output. Jika tidak, geometri akan dihilangkan (menghemat memori).

Contoh

Code Editor (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());

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

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())