ee.FeatureCollection.classify

Classifies each feature in a collection.

UsageReturns
FeatureCollection.classify(classifier, outputName)FeatureCollection
ArgumentTypeDetails
this: featuresFeatureCollection

The collection of features to classify. Each feature must contain all the properties in the classifier's schema.

classifierClassifier

The classifier to use.

outputNameString, default: "classification"

The name of the output property to be added. This argument is ignored if the classifier has more than one output.

Examples

Code Editor (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());