AI-generated Key Takeaways
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This function classifies each feature within a collection.
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It takes a FeatureCollection and a Classifier as input.
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The output is a FeatureCollection with an added classification property.
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The name of the output property can be specified, defaulting to "classification".
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The examples demonstrate classifying features derived from satellite imagery and computing an error matrix to evaluate the classification accuracy.
| Usage | Returns |
|---|---|
FeatureCollection.
classify
(classifier, outputName
)
|
FeatureCollection |
| Argument | Type | Details |
|---|---|---|
|
this:
features
|
FeatureCollection | The collection of features to classify. Each feature must contain all the properties in the classifier's schema. |
classifier
|
Classifier | The classifier to use. |
outputName
|
String, 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 ());
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 ())
