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ee.Classifier.minimumDistance

Creates a minimum distance classifier for the given distance metric. In CLASSIFICATION mode, the nearest class is returned. In REGRESSION mode, the distance to the nearest class center is returned. In RAW mode, the distance to every class center is returned.

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.
'manhattan' - Manhattan distance from the unnormalized 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 
 );

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

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Examples

Code Editor (JavaScript)

Colab (Python)

ee.Classifier.minimumDistance