ee.FeatureCollection.classify

 : classe chaque caractéristique d'une collection.

Utilisation	Renvoie
FeatureCollection.classify(classifier, outputName)	FeatureCollection

Argument	Type	Détails
ceci : features	FeatureCollection	Collection de caractéristiques à classer. Chaque caractéristique doit contenir toutes les propriétés du schéma du classificateur.
classifier	Classificateur	Classificateur à utiliser.
outputName	Chaîne, valeur par défaut : "classification"	Nom de la propriété de sortie à ajouter. Cet argument est ignoré si le classificateur comporte plusieurs sorties.

Exemples

Éditeur de code (JavaScript)

/**
 * Classifies features in a FeatureCollection and computes an error matrix.
 */

// Combine Landsat and NLCD images using only the bands representing
// predictor variables (spectral reflectance) and target labels (land cover).
var spectral =
    ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_038032_20160820').select('SR_B[1-7]');
var landcover =
    ee.Image('USGS/NLCD_RELEASES/2016_REL/2016').select('landcover');
var sampleSource = spectral.addBands(landcover);

// Sample the combined images to generate a FeatureCollection.
var sample = sampleSource.sample({
  region: spectral.geometry(),  // sample only from within Landsat image extent
  scale: 30,
  numPixels: 2000,
  geometries: true
})
// Add a random value column with uniform distribution for hold-out
// training/validation splitting.
.randomColumn({distribution: 'uniform'});
print('Sample for classifier development', sample);

// Split out ~80% of the sample for training the classifier.
var training = sample.filter('random < 0.8');
print('Training set', training);

// Train a random forest classifier.
var classifier = ee.Classifier.smileRandomForest(10).train({
  features: training,
  classProperty: landcover.bandNames().get(0),
  inputProperties: spectral.bandNames()
});

// Classify the sample.
var predictions = sample.classify(
    {classifier: classifier, outputName: 'predicted_landcover'});
print('Predictions', predictions);

// Split out the validation feature set.
var validation = predictions.filter('random >= 0.8');
print('Validation set', validation);

// Get a list of possible class values to use for error matrix axis labels.
var order = sample.aggregate_array('landcover').distinct().sort();
print('Error matrix axis labels', order);

// Compute an error matrix that compares predicted vs. expected values.
var errorMatrix = validation.errorMatrix({
  actual: landcover.bandNames().get(0),
  predicted: 'predicted_landcover',
  order: order
});
print('Error matrix', errorMatrix);

// Compute accuracy metrics from the error matrix.
print("Overall accuracy", errorMatrix.accuracy());
print("Consumer's accuracy", errorMatrix.consumersAccuracy());
print("Producer's accuracy", errorMatrix.producersAccuracy());
print("Kappa", errorMatrix.kappa());

Configuration de Python

Consultez la page Environnement Python pour en savoir plus sur l'API Python et sur l'utilisation de geemap pour le développement interactif.

import ee
import geemap.core as geemap

Colab (Python)

# Classifies features in a FeatureCollection and computes an error matrix.

# Combine Landsat and NLCD images using only the bands representing
# predictor variables (spectral reflectance) and target labels (land cover).
spectral = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_038032_20160820').select(
    'SR_B[1-7]')
landcover = ee.Image('USGS/NLCD_RELEASES/2016_REL/2016').select('landcover')
sample_source = spectral.addBands(landcover)

# Sample the combined images to generate a FeatureCollection.
sample = sample_source.sample(**{
    # sample only from within Landsat image extent
    'region': spectral.geometry(),
    'scale': 30,
    'numPixels': 2000,
    'geometries': True
})
# Add a random value column with uniform distribution for hold-out
# training/validation splitting.
sample = sample.randomColumn(**{'distribution': 'uniform'})
display('Sample for classifier development:', sample)

# Split out ~80% of the sample for training the classifier.
training = sample.filter('random < 0.8')
display('Training set:', training)

# Train a random forest classifier.
classifier = ee.Classifier.smileRandomForest(10).train(**{
    'features': training,
    'classProperty': landcover.bandNames().get(0),
    'inputProperties': spectral.bandNames()
})

# Classify the sample.
predictions = sample.classify(
    **{'classifier': classifier, 'outputName': 'predicted_landcover'})
display('Predictions:', predictions)

# Split out the validation feature set.
validation = predictions.filter('random >= 0.8')
display('Validation set:', validation)

# Get a list of possible class values to use for error matrix axis labels.
order = sample.aggregate_array('landcover').distinct().sort()
display('Error matrix axis labels:', order)

# Compute an error matrix that compares predicted vs. expected values.
error_matrix = validation.errorMatrix(**{
    'actual': landcover.bandNames().get(0),
    'predicted': 'predicted_landcover',
    'order': order
})
display('Error matrix:', error_matrix)

# Compute accuracy metrics from the error matrix.
display('Overall accuracy:', error_matrix.accuracy())
display('Consumer\'s accuracy:', error_matrix.consumersAccuracy())
display('Producer\'s accuracy:', error_matrix.producersAccuracy())
display('Kappa:', error_matrix.kappa())