ee.FeatureCollection.cluster

Clustert jedes Feature in einer Sammlung und fügt jedem Feature eine neue Spalte mit der Clusternummer hinzu, der es zugewiesen wurde.

Nutzung	Ausgabe
FeatureCollection.cluster(clusterer, outputName)	FeatureCollection

Argument	Typ	Details
So gehts: features	FeatureCollection	Die Sammlung von zu clusternden Features. Jedes Feature muss alle Attribute im Schema des Clusterers enthalten.
clusterer	Clusterer	Der zu verwendende Clusterer.
outputName	String, Standard: „cluster“	Der Name des hinzuzufügenden Ausgabeattributs.

Beispiele

Code-Editor (JavaScript)

// Import a Sentinel-2 surface reflectance image.
var image = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG');

// Get the image geometry to define the geographical bounds of a point sample.
var imageBounds = image.geometry();

// Sample the image at a set of random points; a feature collection is returned.
var pointSampleFc = image.sample(
    {region: imageBounds, scale: 20, numPixels: 1000, geometries: true});

// Instantiate a k-means clusterer and train it.
var clusterer = ee.Clusterer.wekaKMeans(5).train(pointSampleFc);

// Cluster the input using the trained clusterer; optionally specify the name
// of the output cluster ID property.
var clusteredFc = pointSampleFc.cluster(clusterer, 'spectral_cluster');

print('Note added "spectral_cluster" property for an example feature',
      clusteredFc.first().toDictionary());

// Visualize the clusters by applying a unique color to each cluster ID.
var palette = ee.List(['8dd3c7', 'ffffb3', 'bebada', 'fb8072', '80b1d3']);
var clusterVis = clusteredFc.map(function(feature) {
  return feature.set('style', {
    color: palette.get(feature.get('spectral_cluster')),
  });
}).style({styleProperty: 'style'});

// Display the points colored by cluster ID with the S2 image.
Map.setCenter(-122.35, 37.47, 9);
Map.addLayer(image, {bands: ['B4', 'B3', 'B2'], min: 0, max: 1500}, 'S2 image');
Map.addLayer(clusterVis, null, 'Clusters');

Python einrichten

Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite Python-Umgebung.

import ee
import geemap.core as geemap

Colab (Python)

# Import a Sentinel-2 surface reflectance image.
image = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')

# Get the image geometry to define the geographical bounds of a point sample.
image_bounds = image.geometry()

# Sample the image at a set of random points a feature collection is returned.
point_sample_fc = image.sample(
    region=image_bounds, scale=20, numPixels=1000, geometries=True
)

# Instantiate a k-means clusterer and train it.
clusterer = ee.Clusterer.wekaKMeans(5).train(point_sample_fc)

# Cluster the input using the trained clusterer optionally specify the name
# of the output cluster ID property.
clustered_fc = point_sample_fc.cluster(clusterer, 'spectral_cluster')

display(
    'Note added "spectral_cluster" property for an example feature',
    clustered_fc.first().toDictionary(),
)

# Visualize the clusters by applying a unique color to each cluster ID.
palette = ee.List(['8dd3c7', 'ffffb3', 'bebada', 'fb8072', '80b1d3'])
cluster_vis = clustered_fc.map(
    lambda feature: feature.set(
        'style', {'color': palette.get(feature.get('spectral_cluster'))}
    )
).style(styleProperty='style')

# Display the points colored by cluster ID with the S2 image.
m = geemap.Map()
m.set_center(-122.35, 37.47, 9)
m.add_layer(
    image, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 1500}, 'S2 image'
)
m.add_layer(cluster_vis, None, 'Clusters')
m