ee.FeatureCollection.cluster

는 컬렉션의 각 특성을 클러스터링하여 클러스터 번호가 포함된 새 열을 각 특성에 추가합니다.

사용반환 값
FeatureCollection.cluster(clusterer, outputName)FeatureCollection
인수유형세부정보
다음과 같은 경우: featuresFeatureCollection클러스터링할 특성의 컬렉션입니다. 각 기능에는 클러스터러의 스키마에 있는 모든 속성이 포함되어야 합니다.
clusterer클러스터러사용할 클러스터러입니다.
outputName문자열, 기본값: 'cluster'추가할 출력 속성의 이름입니다.

예

코드 편집기 (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 설정

Python API 및 geemap를 사용한 대화형 개발에 관한 자세한 내용은 Python 환경 페이지를 참고하세요.

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