ee.FeatureCollection.errorMatrix

Calcula uma matriz de erros 2D para uma coleção comparando duas colunas dela: uma com os valores reais e outra com os valores previstos. Os valores precisam ser números inteiros pequenos e contíguos, começando em 0. O eixo 0 (as linhas) da matriz corresponde aos valores reais, e o eixo 1 (as colunas) aos valores previstos.

Uso	Retorna
FeatureCollection.errorMatrix(actual, predicted, order)	ConfusionMatrix

Argumento	Tipo	Detalhes
isso: collection	FeatureCollection	A coleção de entrada.
actual	String	O nome da propriedade que contém o valor real.
predicted	String	O nome da propriedade que contém o valor previsto.
order	Lista, padrão: nulo	Uma lista dos valores esperados. Se esse argumento não for especificado, os valores serão considerados contíguos e abrangerão o intervalo de 0 a maxValue. Se especificado, somente os valores correspondentes a essa lista serão usados, e a matriz terá dimensões e ordem correspondentes a essa lista.

Exemplos

Editor de código (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());

Configuração do Python

Consulte a página Ambiente Python para informações sobre a API Python e como usar geemap para desenvolvimento interativo.

import ee
import geemap.core as geemap

Colab (Python)

from pprint import pprint

# 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'})
print('Sample for classifier development:', sample.getInfo())

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

# 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'})
print('Predictions:', predictions.getInfo())

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

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

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

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