Code Editor (JavaScript)
// A Sentinel-2 surface reflectance image, reflectance bands selected,
// serves as the source for training and prediction in this contrived example.
var img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')
.select('B.*');
// ESA WorldCover land cover map, used as label source in classifier training.
var lc = ee.Image('ESA/WorldCover/v100/2020');
// Remap the land cover class values to a 0-based sequential series.
var classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];
var remapValues = ee.List.sequence(0, 10);
var label = 'lc';
lc = lc.remap(classValues, remapValues).rename(label).toByte();
// Add land cover as a band of the reflectance image and sample 100 pixels at
// 10 m scale from each land cover class within a region of interest.
var roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838);
var sample = img.addBands(lc).stratifiedSample({
numPoints: 100,
classBand: label,
region: roi,
scale: 10,
geometries: true
});
// Add a random value field to the sample and use it to approximately split 80%
// of the features into a training set and 20% into a validation set.
sample = sample.randomColumn();
var trainingSample = sample.filter('random <= 0.8');
var validationSample = sample.filter('random > 0.8');
// Train a minimum distance classifier (Mahalanobis distance metric) from
// the training sample.
var trainedClassifier = ee.Classifier.minimumDistance('mahalanobis').train({
features: trainingSample,
classProperty: label,
inputProperties: img.bandNames()
});
// Get information about the trained classifier.
print('Results of trained classifier', trainedClassifier.explain());
// Get a confusion matrix and overall accuracy for the training sample.
var trainAccuracy = trainedClassifier.confusionMatrix();
print('Training error matrix', trainAccuracy);
print('Training overall accuracy', trainAccuracy.accuracy());
// Get a confusion matrix and overall accuracy for the validation sample.
validationSample = validationSample.classify(trainedClassifier);
var validationAccuracy = validationSample.errorMatrix(label, 'classification');
print('Validation error matrix', validationAccuracy);
print('Validation accuracy', validationAccuracy.accuracy());
// Classify the reflectance image from the trained classifier.
var imgClassified = img.classify(trainedClassifier);
// Add the layers to the map.
var classVis = {
min: 0,
max: 10,
palette: ['006400' ,'ffbb22', 'ffff4c', 'f096ff', 'fa0000', 'b4b4b4',
'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']
};
Map.setCenter(-122.184, 37.796, 12);
Map.addLayer(img, {bands: ['B11', 'B8', 'B3'], min: 100, max: 3500}, 'img');
Map.addLayer(lc, classVis, 'lc');
Map.addLayer(imgClassified, classVis, 'Classified');
Map.addLayer(roi, {color: 'white'}, 'ROI', false, 0.5);
Map.addLayer(trainingSample, {color: 'black'}, 'Training sample', false);
Map.addLayer(validationSample, {color: 'white'}, 'Validation sample', false);
Python setup
See the
Python Environment page for information on the Python API and using
geemap for interactive development.
import ee
import geemap.core as geemap
Colab (Python)
# A Sentinel-2 surface reflectance image, reflectance bands selected,
# serves as the source for training and prediction in this contrived example.
img = ee.Image(
'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG'
).select('B.*')
# ESA WorldCover land cover map, used as label source in classifier training.
lc = ee.Image('ESA/WorldCover/v100/2020')
# Remap the land cover class values to a 0-based sequential series.
class_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]
remap_values = ee.List.sequence(0, 10)
label = 'lc'
lc = lc.remap(class_values, remap_values).rename(label).toByte()
# Add land cover as a band of the reflectance image and sample 100 pixels at
# 10 m scale from each land cover class within a region of interest.
roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838)
sample = img.addBands(lc).stratifiedSample(
numPoints=100, classBand=label, region=roi, scale=10, geometries=True
)
# Add a random value field to the sample and use it to approximately split 80%
# of the features into a training set and 20% into a validation set.
sample = sample.randomColumn()
training_sample = sample.filter('random <= 0.8')
validation_sample = sample.filter('random > 0.8')
# Train a minimum distance classifier (Mahalanobis distance metric) from
# the training sample.
trained_classifier = ee.Classifier.minimumDistance('mahalanobis').train(
features=training_sample,
classProperty=label,
inputProperties=img.bandNames(),
)
# Get information about the trained classifier.
display('Results of trained classifier', trained_classifier.explain())
# Get a confusion matrix and overall accuracy for the training sample.
train_accuracy = trained_classifier.confusionMatrix()
display('Training error matrix', train_accuracy)
display('Training overall accuracy', train_accuracy.accuracy())
# Get a confusion matrix and overall accuracy for the validation sample.
validation_sample = validation_sample.classify(trained_classifier)
validation_accuracy = validation_sample.errorMatrix(label, 'classification')
display('Validation error matrix', validation_accuracy)
display('Validation accuracy', validation_accuracy.accuracy())
# Classify the reflectance image from the trained classifier.
img_classified = img.classify(trained_classifier)
# Add the layers to the map.
class_vis = {
'min': 0,
'max': 10,
'palette': [
'006400',
'ffbb22',
'ffff4c',
'f096ff',
'fa0000',
'b4b4b4',
'f0f0f0',
'0064c8',
'0096a0',
'00cf75',
'fae6a0',
],
}
m = geemap.Map()
m.set_center(-122.184, 37.796, 12)
m.add_layer(
img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 3500}, 'img'
)
m.add_layer(lc, class_vis, 'lc')
m.add_layer(img_classified, class_vis, 'Classified')
m.add_layer(roi, {'color': 'white'}, 'ROI', False, 0.5)
m.add_layer(training_sample, {'color': 'black'}, 'Training sample', False)
m.add_layer(
validation_sample, {'color': 'white'}, 'Validation sample', False
)
m