Usage | Returns |
---|---|
ee.Classifier.minimumDistance(metric, kNearest) | Classifier |
Argument | Type | Details |
---|---|---|
metric | String, default: "euclidean" | The distance metric to use. Options are:
'euclidean' - euclidean distance from the unnormalized class mean. 'cosine' - spectral angle from the unnormalized class mean. 'mahalanobis' - Mahalanobis distance from the class mean. |
kNearest | Integer, default: 1 | If greater than 1, the result will contain an array of the k nearest neighbors or distances, based on the output mode setting. if kNearest is greater than the total number of classes, it will be set equal to the number of classes. |
Examples
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);